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The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.","impact":"Use The Anthropic leader who built Claude Code ditched prompting - now he writes loops to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from thenewstack.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"whole-loop","audience":"newcomer","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Janakiram MSV","publication_date":"2026-06-10","publication_year":"2026","publication_venue":"","publisher":"The New Stack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0020","title":"Engineering for Agents That Never Sleep","url":"https://nader.substack.com/p/engineering-for-agents-that-never","canonical_url":"https://nader.substack.com/p/engineering-for-agents-that-never","annotation":"Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","key_contribution":"Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","novelty":"Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","impact":"Use Engineering for Agents That Never Sleep to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from nader.substack.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"trigger;verification;escalation","audience":"newcomer","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Nader Dabit","publication_date":"","publication_year":"","publication_venue":"","publisher":"Substack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0021","title":"Loop Engineering Orange Book","url":"https://github.com/alchaincyf/loop-engineering-orange-book","canonical_url":"https://github.com/alchaincyf/loop-engineering-orange-book","annotation":"Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","key_contribution":"Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","novelty":"The resource is directly reusable as a starting artifact. Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","impact":"Use Loop Engineering Orange Book to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Inspectable GitHub source (1,024 stars; 99 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"whole-loop","audience":"newcomer","evidence_class":"practitioner-analysis","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-15","publication_year":"2026","publication_venue":"alchaincyf/loop-engineering-orange-book","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"alchaincyf/loop-engineering-orange-book","github_stars":"1024","arxiv_id":"","date_added":""},{"row_id":"ale-0022","title":"How I AI: How to Write AI Agent Loops in Claude Code and Codex","url":"https://www.lennysnewsletter.com/p/how-i-ai-how-to-write-ai-agent-loops","canonical_url":"https://www.lennysnewsletter.com/p/how-i-ai-how-to-write-ai-agent-loops","annotation":"Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","key_contribution":"Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","novelty":"The trigger or cadence is explicit, making the workflow recurring rather than one-off. Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","impact":"Use How I AI: How to Write AI Agent Loops in Claude Code and Codex to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from www.lennysnewsletter.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"objective;trigger;delegation","audience":"newcomer","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Lenny Rachitsky","publication_date":"","publication_year":"","publication_venue":"","publisher":"lennysnewsletter.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0023","title":"Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control","url":"https://arxiv.org/abs/2607.14890","canonical_url":"https://arxiv.org/abs/2607.14890","annotation":"Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","key_contribution":"Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","impact":"Use Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.14890; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"verification;exit","audience":"newcomer;researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jek Huang; Jeffery Hsia; Jiayi Sun; Freddie Shi; Wei Huang; Ian H. White","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"48 pages, 10 figures, 29 numbered tables. Preprint v1","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14890","date_added":"2026-07-17"},{"row_id":"ale-0024","title":"PR babysitter","url":"patterns/pr-babysitter.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/pr-babysitter.md","annotation":"Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","key_contribution":"Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","impact":"Use PR babysitter to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"whole-loop","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0025","title":"CI repair loop","url":"patterns/ci-repair-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/ci-repair-loop.md","annotation":"Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","key_contribution":"Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","impact":"Use CI repair loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"escalation","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0026","title":"Docs drift collector","url":"patterns/docs-drift-collector.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/docs-drift-collector.md","annotation":"Finds mismatches between docs and code, proposes small patches, and verifies examples.","key_contribution":"Finds mismatches between docs and code, proposes small patches, and verifies examples.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Finds mismatches between docs and code, proposes small patches, and verifies examples.","impact":"Use Docs drift collector to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"verification","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0027","title":"Deploy verifier","url":"patterns/deploy-verifier.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/deploy-verifier.md","annotation":"Watches rollout signals, compares them with release expectations, and stops on anomalies.","key_contribution":"Watches rollout signals, compares them with release expectations, and stops on anomalies.","novelty":"Verification is promoted from a final check to a loop-control signal. Watches rollout signals, compares them with release expectations, and stops on anomalies.","impact":"Use Deploy verifier to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"exit","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0028","title":"Feedback clusterer","url":"patterns/feedback-clusterer.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/feedback-clusterer.md","annotation":"Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","key_contribution":"Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","impact":"Use Feedback clusterer to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"whole-loop","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0029","title":"Dependency triage loop","url":"patterns/dependency-triage-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/dependency-triage-loop.md","annotation":"Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","key_contribution":"Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","impact":"Use Dependency triage loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"intake;verification;escalation","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0030","title":"Evaluation regression loop","url":"patterns/evaluation-regression-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/evaluation-regression-loop.md","annotation":"Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","key_contribution":"Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","impact":"Use Evaluation regression loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"verification","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0031","title":"Benchmark optimization loop","url":"patterns/benchmark-optimization-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/benchmark-optimization-loop.md","annotation":"Runs bounded experiments against a frozen benchmark and accepts only reproducible gains with correctness intact.","key_contribution":"Runs bounded experiments against a frozen benchmark and accepts only reproducible gains with correctness intact.","novelty":"The work turns loop quality into a measurable task or score. Runs bounded experiments against a frozen benchmark and accepts only reproducible gains with correctness intact.","impact":"Use Benchmark optimization loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"verification","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0032","title":"Security review loop","url":"patterns/security-review-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/security-review-loop.md","annotation":"Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","key_contribution":"Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","impact":"Use Security review loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"workspace;escalation","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0033","title":"Adversarial red-team loop","url":"patterns/adversarial-red-team-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/adversarial-red-team-loop.md","annotation":"Discovers agent failures inside an authorized sandbox, then independently reproduces, minimizes, and reports them.","key_contribution":"Discovers agent failures inside an authorized sandbox, then independently reproduces, minimizes, and reports them.","novelty":"Execution isolation and permission boundaries are part of the design. Discovers agent failures inside an authorized sandbox, then independently reproduces, minimizes, and reports them.","impact":"Use Adversarial red-team loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"intake;workspace","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0034","title":"Accessibility regression loop","url":"patterns/accessibility-regression-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/accessibility-regression-loop.md","annotation":"Repairs reproducible accessibility regressions while preserving required human review for non-automatable criteria.","key_contribution":"Repairs reproducible accessibility regressions while preserving required human review for non-automatable criteria.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Repairs reproducible accessibility regressions while preserving required human review for non-automatable criteria.","impact":"Use Accessibility regression loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"escalation","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0035","title":"Cost-control loop","url":"patterns/cost-control-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/cost-control-loop.md","annotation":"Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","key_contribution":"Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","impact":"Use Cost-control loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"budget","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0036","title":"Performance regression loop","url":"patterns/performance-regression-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/performance-regression-loop.md","annotation":"Profiles a measured regression and verifies a narrow fix against the same controlled workload and correctness gates.","key_contribution":"Profiles a measured regression and verifies a narrow fix against the same controlled workload and correctness gates.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Profiles a measured regression and verifies a narrow fix against the same controlled workload and correctness gates.","impact":"Use Performance regression loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"verification","audience":"builder","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0037","title":"Bug hunting loop","url":"patterns/bug-hunting-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/bug-hunting-loop.md","annotation":"Discovers, reproduces, minimizes, and reports bugs with concrete evidence.","key_contribution":"Discovers, reproduces, minimizes, and reports bugs with concrete evidence.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. 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Claude SDK overview for tool-using agents, subagents, state, permissions, and streaming.","impact":"Use Building agents with the Claude Agent SDK to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from code.claude.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;delegation;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0082","title":"How the agent loop works","url":"https://code.claude.com/docs/en/agent-sdk/agent-loop","canonical_url":"https://code.claude.com/docs/en/agent-sdk/agent-loop","annotation":"Official walkthrough of the inner agent loop that outer recurring loops build on.","key_contribution":"Official walkthrough of the inner agent loop that outer recurring loops build on.","novelty":"Primary-source operational guidance rather than commentary. Official walkthrough of the inner agent loop that outer recurring loops build on.","impact":"Use How the agent loop works to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from code.claude.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude Code Docs","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0083","title":"Extend Claude with skills","url":"https://code.claude.com/docs/en/skills","canonical_url":"https://code.claude.com/docs/en/skills","annotation":"Claude Code skill system for reusable loop instructions and assets.","key_contribution":"Claude Code skill system for reusable loop instructions and assets.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Claude Code skill system for reusable loop instructions and assets.","impact":"Use Extend Claude with skills to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from code.claude.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude Code Docs","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0084","title":"Create custom subagents","url":"https://code.claude.com/docs/en/sub-agents","canonical_url":"https://code.claude.com/docs/en/sub-agents","annotation":"Claude Code custom subagents with isolated context, model choice, and tool permissions.","key_contribution":"Claude Code custom subagents with isolated context, model choice, and tool permissions.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Claude Code custom subagents with isolated context, model choice, and tool permissions.","impact":"Use Create custom subagents to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from code.claude.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude Code Docs","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0085","title":"Writing effective tools for AI agents","url":"https://www.anthropic.com/engineering/writing-tools-for-agents","canonical_url":"https://www.anthropic.com/engineering/writing-tools-for-agents","annotation":"Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.","key_contribution":"Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.","impact":"Use Writing effective tools for AI agents to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from www.anthropic.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0086","title":"Introducing advanced tool use on the Claude Developer Platform","url":"https://www.anthropic.com/engineering/advanced-tool-use?e45d281a_page=3","canonical_url":"https://www.anthropic.com/engineering/advanced-tool-use?e45d281a_page=3","annotation":"Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","key_contribution":"Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","impact":"Use Introducing advanced tool use on the Claude Developer Platform to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from www.anthropic.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0087","title":"Effective harnesses for long-running agents","url":"https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents","canonical_url":"https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents","annotation":"Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","key_contribution":"Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","impact":"Use Effective harnesses for long-running agents to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from www.anthropic.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"context;verification","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0088","title":"Claude Code best practices","url":"https://code.claude.com/docs/en/best-practices","canonical_url":"https://code.claude.com/docs/en/best-practices","annotation":"Widely cited workflow guidance that underlies many recurring Claude Code loops.","key_contribution":"Widely cited workflow guidance that underlies many recurring Claude Code loops.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Widely cited workflow guidance that underlies many recurring Claude Code loops.","impact":"Use Claude Code best practices to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from code.claude.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude Code Docs","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0089","title":"Claude Managed Agents: Scheduled Deployments and Vaults","url":"https://claude.com/blog/whats-new-in-claude-managed-agents","canonical_url":"https://claude.com/blog/whats-new-in-claude-managed-agents","annotation":"Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","key_contribution":"Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","novelty":"The trigger or cadence is explicit, making the workflow recurring rather than one-off. 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Launch overview of the issue-to-PR delegation loop, including iteration on review feedback.","impact":"Use GitHub Copilot: Meet the new coding agent to choose an implementation surface for repeatable agent work.","signal":"Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"intake;delegation","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Thomas Dohmke","publication_date":"2025-05-19","publication_year":"2025","publication_venue":"","publisher":"The GitHub Blog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0098","title":"GitHub Copilot for Jira Is Now Generally Available","url":"https://github.blog/changelog/2026-06-25-github-copilot-for-jira-is-now-generally-available/","canonical_url":"https://github.blog/changelog/2026-06-25-github-copilot-for-jira-is-now-generally-available/","annotation":"General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.","key_contribution":"General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. 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Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.","impact":"Use Copilot Agent Session Streaming (Public Preview) to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from github.blog; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;delegation","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"The GitHub Blog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0100","title":"Security Reviews in the GitHub Copilot App","url":"https://github.blog/changelog/2026-07-14-security-reviews-now-available-in-the-github-copilot-app","canonical_url":"https://github.blog/changelog/2026-07-14-security-reviews-now-available-in-the-github-copilot-app/","annotation":"Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","key_contribution":"Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","impact":"Use Security Reviews in the GitHub Copilot App to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from github.blog; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"The GitHub Blog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0101","title":"Cursor cloud agents","url":"https://cursor.com/docs/cloud-agent","canonical_url":"https://cursor.com/docs/cloud-agent","annotation":"Remote agents that work asynchronously in isolated environments and hand results back for review.","key_contribution":"Remote agents that work asynchronously in isolated environments and hand results back for review.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Remote agents that work asynchronously in isolated environments and hand results back for review.","impact":"Use Cursor cloud agents to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from cursor.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor Documentation","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0102","title":"Cursor 3.8: Improvements to Cursor Automations","url":"https://cursor.com/changelog/06-18-26","canonical_url":"https://cursor.com/changelog/06-18-26","annotation":"Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","key_contribution":"Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","impact":"Use Cursor 3.8: Improvements to Cursor Automations to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from cursor.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"trigger;workspace","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0103","title":"Expanding Our Long-Running Agents Research Preview","url":"https://cursor.com/blog/long-running-agents","canonical_url":"https://cursor.com/blog/long-running-agents","annotation":"Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","key_contribution":"Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","impact":"Use Expanding Our Long-Running Agents Research Preview to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"escalation","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Cursor Team","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0104","title":"Cursor 3.11: Side Chats, Transcript Search, and Cloud Agent Hooks","url":"https://cursor.com/changelog/side-chat","canonical_url":"https://cursor.com/changelog/side-chat","annotation":"Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","key_contribution":"Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","impact":"Use Cursor 3.11: Side Chats, Transcript Search, and Cloud Agent Hooks to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from cursor.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"delegation;exit","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0105","title":"Jules","url":"https://jules.google/docs","canonical_url":"https://jules.google/docs","annotation":"Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","key_contribution":"Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","impact":"Use Jules to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from jules.google; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Jules","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0106","title":"Devin Docs","url":"https://docs.devin.ai/get-started/devin-intro","canonical_url":"https://docs.devin.ai/get-started/devin-intro","annotation":"Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","key_contribution":"Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","impact":"Use Devin Docs to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from docs.devin.ai; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Devin Docs","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0107","title":"Amp: Agents, Anywhere","url":"https://ampcode.com/news/agents-anywhere","canonical_url":"https://ampcode.com/news/agents-anywhere","annotation":"Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.","key_contribution":"Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.","impact":"Use Amp: Agents, Anywhere to choose an implementation surface for repeatable agent work.","signal":"Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"ampcode.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0108","title":"ReAct: Synergizing Reasoning and Acting in Language Models","url":"https://arxiv.org/abs/2210.03629","canonical_url":"https://openreview.net/forum?id=WE_vluYUL-X","annotation":"Foundational reason-act-observe loop for tool-using language agents.","key_contribution":"Foundational reason-act-observe loop for tool-using language agents.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Foundational reason-act-observe loop for tool-using language agents.","impact":"Use ReAct: Synergizing Reasoning and Acting in Language Models to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2210.03629; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"workspace","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Shunyu Yao; Jeffrey Zhao; Dian Yu; Nan Du; Izhak Shafran; Karthik Narasimhan; Yuan Cao","publication_date":"2023","publication_year":"2023","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"OpenReview proceedings record","github_repo":"","github_stars":"","arxiv_id":"2210.03629","date_added":""},{"row_id":"ale-0109","title":"Reflexion: Language Agents with Verbal Reinforcement Learning","url":"https://arxiv.org/abs/2303.11366","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2023/hash/1b44b878bb782e6954cd888628510e90-Abstract-Conference.html","annotation":"Converts environment feedback into written reflections stored in memory for future attempts.","key_contribution":"Converts environment feedback into written reflections stored in memory for future attempts.","novelty":"Persistent memory is treated as an external runtime artifact. Converts environment feedback into written reflections stored in memory for future attempts.","impact":"Use Reflexion: Language Agents with Verbal Reinforcement Learning to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2303.11366; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"context","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Noah Shinn; Federico Cassano; Edward Berman; Ashwin Gopinath; Karthik Narasimhan; Shunyu Yao","publication_date":"2023","publication_year":"2023","publication_venue":"Advances in Neural Information Processing Systems 36 (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"10.52202/075280-0377","publication_note":"Published in Advances in Neural Information Processing Systems 36 (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"NeurIPS proceedings and DOI records","github_repo":"","github_stars":"","arxiv_id":"2303.11366","date_added":""},{"row_id":"ale-0110","title":"Self-Refine: Iterative Refinement with Self-Feedback","url":"https://arxiv.org/abs/2303.17651","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2023/hash/91edff07232fb1b55a505a9e9f6c0ff3-Abstract-Conference.html","annotation":"Generate-feedback-refine loop where a model improves outputs over repeated passes.","key_contribution":"Generate-feedback-refine loop where a model improves outputs over repeated passes.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Generate-feedback-refine loop where a model improves outputs over repeated passes.","impact":"Use Self-Refine: Iterative Refinement with Self-Feedback to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2303.17651; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Aman Madaan; Niket Tandon; Prakhar Gupta; Skyler Hallinan; Luyu Gao; Sarah Wiegreffe; Uri Alon; Nouha Dziri; Shrimai Prabhumoye; Yiming Yang; Shashank Gupta; Bodhisattwa Prasad Majumder; Katherine Hermann; Sean Welleck; Amir Yazdanbakhsh; Peter Clark","publication_date":"2023","publication_year":"2023","publication_venue":"Advances in Neural Information Processing Systems 36 (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"","publication_note":"Published in Advances in Neural Information Processing Systems 36 (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"NeurIPS proceedings record","github_repo":"","github_stars":"","arxiv_id":"2303.17651","date_added":""},{"row_id":"ale-0111","title":"CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing","url":"https://arxiv.org/abs/2305.11738","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/fef126561bbf9d4467dbb8d27334b8fe-Abstract-Conference.html","annotation":"Uses tools to ground critique and correction rather than relying only on introspection.","key_contribution":"Uses tools to ground critique and correction rather than relying only on introspection.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Uses tools to ground critique and correction rather than relying only on introspection.","impact":"Use CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2305.11738; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Zhibin Gou; Zhihong Shao; Yeyun Gong; Yelong Shen; Yujiu Yang; Nan Duan; Weizhu Chen","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2305.11738","date_added":""},{"row_id":"ale-0112","title":"Tree of Thoughts","url":"https://arxiv.org/abs/2305.10601","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2023/hash/271db9922b8d1f4dd7aaef84ed5ac703-Abstract.html","annotation":"Search over multiple reasoning branches; relevant when loop design needs exploration before committing.","key_contribution":"Search over multiple reasoning branches; relevant when loop design needs exploration before committing.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Search over multiple reasoning branches; relevant when loop design needs exploration before committing.","impact":"Use Tree of Thoughts to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2305.10601; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Shunyu Yao; Dian Yu; Jeffrey Zhao; Izhak Shafran; Thomas L. Griffiths; Yuan Cao; Karthik Narasimhan","publication_date":"2023","publication_year":"2023","publication_venue":"Advances in Neural Information Processing Systems 36 (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"","publication_note":"Published in Advances in Neural Information Processing Systems 36 (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"NeurIPS proceedings record","github_repo":"","github_stars":"","arxiv_id":"2305.10601","date_added":""},{"row_id":"ale-0113","title":"Graph of Thoughts","url":"https://arxiv.org/abs/2308.09687","canonical_url":"https://ojs.aaai.org/index.php/AAAI/article/view/29720","annotation":"Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","key_contribution":"Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","impact":"Use Graph of Thoughts to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2308.09687; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Maciej Besta; Nils Blach; Ales Kubicek; Robert Gerstenberger; Michal Podstawski; Lukas Gianinazzi; Joanna Gajda; Tomasz Lehmann; Hubert Niewiadomski; Piotr Nyczyk; Torsten Hoefler","publication_date":"2024-03-24","publication_year":"2024","publication_venue":"Proceedings of the AAAI Conference on Artificial Intelligence 38 (AAAI)","publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","doi":"10.1609/aaai.v38i16.29720","publication_note":"Published in Proceedings of the AAAI Conference on Artificial Intelligence 38 (AAAI); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"AAAI proceedings and DOI records","github_repo":"","github_stars":"","arxiv_id":"2308.09687","date_added":""},{"row_id":"ale-0114","title":"Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models","url":"https://arxiv.org/abs/2310.04406","canonical_url":"https://proceedings.mlr.press/v235/zhou24r.html","annotation":"Combines search, action, and environment feedback for language agents.","key_contribution":"Combines search, action, and environment feedback for language agents.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Combines search, action, and environment feedback for language agents.","impact":"Use Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2310.04406; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Andy Zhou; Kai Yan; Michal Shlapentokh-Rothman; Haohan Wang; Yu-Xiong Wang","publication_date":"2024","publication_year":"2024","publication_venue":"Proceedings of the 41st International Conference on Machine Learning (ICML)","publisher":"PMLR","doi":"","publication_note":"Published in Proceedings of the 41st International Conference on Machine Learning (ICML); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"PMLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2310.04406","date_added":""},{"row_id":"ale-0115","title":"Voyager: An Open-Ended Embodied Agent with Large Language Models","url":"https://arxiv.org/abs/2305.16291","canonical_url":"https://openreview.net/forum?id=ehfRiF0R3a","annotation":"Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","key_contribution":"Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","impact":"Use Voyager: An Open-Ended Embodied Agent with Large Language Models to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2305.16291; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Guanzhi Wang; Yuqi Xie; Yunfan Jiang; Ajay Mandlekar; Chaowei Xiao; Yuke Zhu; Linxi Fan; Anima Anandkumar","publication_date":"2024","publication_year":"2024","publication_venue":"Transactions on Machine Learning Research (TMLR)","publisher":"OpenReview","doi":"","publication_note":"Published in Transactions on Machine Learning Research (TMLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"TMLR OpenReview record","github_repo":"","github_stars":"","arxiv_id":"2305.16291","date_added":""},{"row_id":"ale-0116","title":"Generative Agents: Interactive Simulacra of Human Behavior","url":"https://arxiv.org/abs/2304.03442","canonical_url":"https://doi.org/10.1145/3586183.3606763","annotation":"Introduces reflection and memory mechanisms for long-running agent behavior.","key_contribution":"Introduces reflection and memory mechanisms for long-running agent behavior.","novelty":"Persistent memory is treated as an external runtime artifact. Introduces reflection and memory mechanisms for long-running agent behavior.","impact":"Use Generative Agents: Interactive Simulacra of Human Behavior to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2304.03442; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"context;escalation","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Joon Sung Park; Joseph C. O'Brien; Carrie J. Cai; Meredith Ringel Morris; Percy Liang; Michael S. Bernstein","publication_date":"2023-10-29","publication_year":"2023","publication_venue":"Proceedings of the 36th ACM Symposium on User Interface Software and Technology (UIST)","publisher":"Association for Computing Machinery","doi":"10.1145/3586183.3606763","publication_note":"Published in Proceedings of the 36th ACM Symposium on User Interface Software and Technology (UIST); the linked arXiv record remains available for open access.","primary_category":"cs.HC","metadata_source":"ACM DOI record","github_repo":"","github_stars":"","arxiv_id":"2304.03442","date_added":""},{"row_id":"ale-0117","title":"Measuring AI Ability to Complete Long Software Tasks","url":"https://arxiv.org/abs/2503.14499","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2025/hash/85069585133c4c168c865e65d72e9775-Abstract-Conference.html","annotation":"METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","key_contribution":"METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","impact":"Use Measuring AI Ability to Complete Long Software Tasks to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2503.14499; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"state;budget;escalation","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Thomas Kwa; Ben West; Joel Becker; Amy Deng; Katharyn Garcia; Max Hasin; Sami Jawhar; Megan Kinniment; Nate Rush; Sydney Von Arx; Ryan Bloom; Thomas Broadley; Haoxing Du; Brian Goodrich; Nikola Jurkovic; Luke Harold Miles; Seraphina Nix; Tao Lin; Chris Painter; Neev Parikh; David Rein; Lucas Jun Koba Sato; Hjalmar Wijk; Daniel M. Ziegler; Elizabeth Barnes; Lawrence Chan","publication_date":"2025","publication_year":"2025","publication_venue":"Advances in Neural Information Processing Systems 38 (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"","publication_note":"Published in Advances in Neural Information Processing Systems 38 (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"NeurIPS proceedings record","github_repo":"","github_stars":"","arxiv_id":"2503.14499","date_added":""},{"row_id":"ale-0118","title":"Measuring AI Ability to Complete Long Tasks","url":"https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/","canonical_url":"https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/","annotation":"Accessible summary of the 50% task-completion time horizon and its doubling trend.","key_contribution":"Accessible summary of the 50% task-completion time horizon and its doubling trend.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Accessible summary of the 50% task-completion time horizon and its doubling trend.","impact":"Use Measuring AI Ability to Complete Long Tasks to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"exit","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2025-03-19","publication_year":"2025","publication_venue":"METR Blog","publisher":"metr.org","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0119","title":"Reflection-Driven Control for Trustworthy Code Agents","url":"https://arxiv.org/abs/2512.21354","canonical_url":"https://openreview.net/forum?id=vUtz66IHD1","annotation":"Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.","key_contribution":"Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.","impact":"Use Reflection-Driven Control for Trustworthy Code Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2512.21354; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Bin Wang; Jiazheng Quan; Xingrui Yu; Hansen Hu; Yuhao; Ivor Tsang","publication_date":"2026","publication_year":"2026","publication_venue":"AAAI Workshop on Trust and Control in Agentic AI (TrustAgent)","publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","doi":"","publication_note":"Published in AAAI Workshop on Trust and Control in Agentic AI (TrustAgent); the linked arXiv record remains available for open access.","primary_category":"cs.CR","metadata_source":"AAAI workshop OpenReview record","github_repo":"","github_stars":"","arxiv_id":"2512.21354","date_added":""},{"row_id":"ale-0120","title":"Hyperagents","url":"https://arxiv.org/abs/2603.19461","canonical_url":"https://arxiv.org/abs/2603.19461","annotation":"Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","key_contribution":"Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","impact":"Use Hyperagents to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2603.19461; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jenny Zhang; Bingchen Zhao; Wannan Yang; Jakob Foerster; Jeff Clune; Minqi Jiang; Sam Devlin; Tatiana Shavrina","publication_date":"2026-03-19","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code at https://github.com/facebookresearch/Hyperagents","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.19461","date_added":""},{"row_id":"ale-0121","title":"PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks","url":"https://arxiv.org/abs/2512.03549","canonical_url":"https://arxiv.org/abs/2512.03549","annotation":"Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.","key_contribution":"Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.","novelty":"The work targets tasks that exceed a single context window or prompt session. Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.","impact":"Use PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2512.03549; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yuki Orimo; Iori Kurata; Hodaka Mori; Ryuhei Okuno; Ryohto Sawada; Daisuke Okanohara","publication_date":"2025-12-03","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2512.03549","date_added":""},{"row_id":"ale-0122","title":"When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents","url":"https://arxiv.org/abs/2603.17104","canonical_url":"https://arxiv.org/abs/2603.17104","annotation":"Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","key_contribution":"Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","novelty":"The work targets tasks that exceed a single context window or prompt session. Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","impact":"Use When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2603.17104; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Lu Yan; Xuan Chen; Xiangyu Zhang","publication_date":"2026-03-17","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.17104","date_added":""},{"row_id":"ale-0123","title":"Reflexion code","url":"https://github.com/noahshinn/reflexion","canonical_url":"https://github.com/noahshinn/reflexion","annotation":"Reference implementation and experiments for verbal reinforcement loops.","key_contribution":"Reference implementation and experiments for verbal reinforcement loops.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Reference implementation and experiments for verbal reinforcement loops.","impact":"Use Reflexion code to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Inspectable GitHub source (3,205 stars; 312 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-03-22","publication_year":"2023","publication_venue":"noahshinn/reflexion","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"noahshinn/reflexion","github_stars":"3205","arxiv_id":"","date_added":""},{"row_id":"ale-0124","title":"Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting","url":"https://arxiv.org/abs/2607.00038","canonical_url":"https://arxiv.org/abs/2607.00038","annotation":"Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","key_contribution":"Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","novelty":"Verification is promoted from a final check to a loop-control signal. Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","impact":"Use Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.00038; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"objective;trigger;context;verification;exit","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Sandeco Macedo","publication_date":"2026-06-28","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00038","date_added":""},{"row_id":"ale-0125","title":"From Question Answering to Task Completion: A Survey on Agent System and Harness Design","url":"https://arxiv.org/abs/2606.20683","canonical_url":"https://arxiv.org/abs/2606.20683","annotation":"Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","key_contribution":"Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","novelty":"Verification is promoted from a final check to a loop-control signal. Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","impact":"Use From Question Answering to Task Completion: A Survey on Agent System and Harness Design to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2606.20683; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"context;verification;state;exit","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jianyuan Guo; Zhiwei Hao; Chengcheng Wang; Cheng Fan; Tingzhang Luo; Hongguang Li; Ying Gao; Hefei Mei; Jiankun Peng; Rongjian Xu; Minjing Dong; Han Wu; Mengyu Zheng; Kai Han; Shiqi Wang; Chang Xu; Yunhe Wang","publication_date":"2026-06-14","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.20683","date_added":""},{"row_id":"ale-0126","title":"MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems","url":"https://arxiv.org/abs/2605.22794","canonical_url":"https://arxiv.org/abs/2605.22794","annotation":"Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","key_contribution":"Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","impact":"Use MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2605.22794; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification;state;escalation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Qianshu Cai; Yonggang Zhang; Xianzhang Jia; Huajiang Zheng; Wei Xue; Jun Song; Xinmei Tian; Yike Guo","publication_date":"2026-05-21","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"12 pages, 3 figures, 2 tables. Preprint. Code: https://github.com/hkgai-official/Moss","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.22794","date_added":""},{"row_id":"ale-0127","title":"METR Time Horizon 1.1","url":"https://metr.org/blog/2026-1-29-time-horizon-1-1/","canonical_url":"https://metr.org/blog/2026-1-29-time-horizon-1-1/","annotation":"Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","key_contribution":"Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","impact":"Use METR Time Horizon 1.1 to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-01-29","publication_year":"2026","publication_venue":"METR Blog","publisher":"metr.org","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0128","title":"MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution","url":"https://arxiv.org/abs/2607.05297","canonical_url":"https://arxiv.org/abs/2607.05297","annotation":"Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","key_contribution":"Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","impact":"Use MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.05297; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Zefeng Wang; Minxi Yan; Jinhe Bi; Sikuan Yan; Volker Tresp; Yunpu Ma","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05297","date_added":""},{"row_id":"ale-0129","title":"SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe","url":"https://arxiv.org/abs/2607.03451","canonical_url":"https://arxiv.org/abs/2607.03451","annotation":"Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","key_contribution":"Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","impact":"Use SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.03451; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yifei Shen; Bo Li; Xinjie Zhang","publication_date":"2026-07-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.03451","date_added":""},{"row_id":"ale-0130","title":"Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops","url":"https://arxiv.org/abs/2607.07663","canonical_url":"https://arxiv.org/abs/2607.07663","annotation":"Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","key_contribution":"Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","impact":"Use Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07663; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Mingguang Chen; Licheng Wang; Bo Qu","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"42 pages, 6 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07663","date_added":""},{"row_id":"ale-0131","title":"From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents","url":"https://arxiv.org/abs/2607.07321","canonical_url":"https://arxiv.org/abs/2607.07321","annotation":"EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","key_contribution":"EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","impact":"Use From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07321; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Haipeng Ding; Yuexiang Xie; Zhewei Wei; Yaliang Li; Bolin Ding","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07321","date_added":""},{"row_id":"ale-0132","title":"TTHE: Test-Time Harness Evolution","url":"https://arxiv.org/abs/2607.08124","canonical_url":"https://arxiv.org/abs/2607.08124","annotation":"Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","key_contribution":"Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","impact":"Use TTHE: Test-Time Harness Evolution to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.08124; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jun Nie; Yonggang Zhang; Jun Song; Qianshu Cai; Dahai Yu; Yike Guo; Xinmei Tian; Bo Han","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"15 pages, 5 figures","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08124","date_added":""},{"row_id":"ale-0133","title":"DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment","url":"https://arxiv.org/abs/2607.07820","canonical_url":"https://arxiv.org/abs/2607.07820","annotation":"Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","key_contribution":"Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","novelty":"Verification is promoted from a final check to a loop-control signal. Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","impact":"Use DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07820; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xinyu Geng; Xuanhua He; Sixiang Chen; Yanjing Xiao; Fan Zhang; Shijue Huang; Haitao Mi; Zhenwen Liang; Tianqing Fang; Yi R. Fung","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07820","date_added":""},{"row_id":"ale-0134","title":"What Makes a Good Bug Report for an AI Agent?","url":"https://arxiv.org/abs/2607.07593","canonical_url":"https://arxiv.org/abs/2607.07593","annotation":"Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","key_contribution":"Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","impact":"Use What Makes a Good Bug Report for an AI Agent? to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07593; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"intake","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Lara Khatib; Noble Saji Mathews; Meiyappan Nagappan; Pengyu Nie; Thomas Zimmermann","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07593","date_added":""},{"row_id":"ale-0135","title":"AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution","url":"https://arxiv.org/abs/2607.08252","canonical_url":"https://arxiv.org/abs/2607.08252","annotation":"Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","key_contribution":"Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","novelty":"State persistence is explicit enough for repeated runs and handoff. Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","impact":"Use AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.08252; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Mengchen Li","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"52 pages, 13 figures/tables, ancillary public-safe evaluation artifacts included","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08252","date_added":""},{"row_id":"ale-0136","title":"Agentic Data Environments","url":"https://arxiv.org/abs/2607.07397","canonical_url":"http://sites.computer.org/debull/A26mar/A26MAR-CD.pdf#page=7","annotation":"Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","key_contribution":"Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","novelty":"State persistence is explicit enough for repeated runs and handoff. Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","impact":"Use Agentic Data Environments to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07397; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"state;budget","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Elaine Ang; Chenxi Huang; Georgios Liargkovas; Jerry Liu; Jinhui Liu; Nikos Pagonas; Charlie Summers; Haonan Wang; Jiakai Xu; Tianle Zhou; Yusen Zhang; Zhou Yu; Zhuo Zhang; Tianyi Peng; Kostis Kaffes; Eugene Wu","publication_date":"2026-03","publication_year":"2026","publication_venue":"IEEE Data Engineering Bulletin 50(1)","publisher":"IEEE","doi":"","publication_note":"Published in IEEE Data Engineering Bulletin 50(1); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"IEEE Data Engineering Bulletin record","github_repo":"","github_stars":"","arxiv_id":"2607.07397","date_added":""},{"row_id":"ale-0137","title":"Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation","url":"https://arxiv.org/abs/2607.08938","canonical_url":"https://arxiv.org/abs/2607.08938","annotation":"Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","key_contribution":"Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","impact":"Use Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.08938; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Chenyang Yang; Xinran Zhao; Tongshuang Wu; Christian Kästner","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08938","date_added":""},{"row_id":"ale-0138","title":"Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills","url":"https://arxiv.org/abs/2607.09065","canonical_url":"https://arxiv.org/abs/2607.09065","annotation":"First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","key_contribution":"First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","impact":"Use Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.09065; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jialun Cao; Xinru Yan; Songqiang Chen; Yaojie Lu; Zhongxin Liu; Shing-Chi Cheung","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09065","date_added":""},{"row_id":"ale-0139","title":"Harness Engineering for Self-Improvement","url":"https://lilianweng.github.io/posts/2026-07-04-harness/","canonical_url":"https://lilianweng.github.io/posts/2026-07-04-harness/","annotation":"Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","key_contribution":"Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","novelty":"Orchestration and control flow are made explicit and inspectable. Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","impact":"Use Harness Engineering for Self-Improvement to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from lilianweng.github.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"delegation","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Lilian Weng","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"","publisher":"lilianweng.github.io","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0140","title":"Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime","url":"https://arxiv.org/abs/2607.11346","canonical_url":"https://arxiv.org/abs/2607.11346","annotation":"Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","key_contribution":"Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","impact":"Use Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.11346; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Chenglin Yu; Li Yin; Ying Yu; Qingxin Fan; RunyangRay Zhong; Hongxia Yang; Ming Li","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"9 pages, 3 figures, 5 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11346","date_added":"2026-07-15"},{"row_id":"ale-0141","title":"Mako: A Self-Evolving Agentic Operating System for Autonomous Web Exploitation","url":"https://arxiv.org/abs/2607.11288","canonical_url":"https://arxiv.org/abs/2607.11288","annotation":"Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","key_contribution":"Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","novelty":"Verification is promoted from a final check to a loop-control signal. Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","impact":"Use Mako: A Self-Evolving Agentic Operating System for Autonomous Web Exploitation to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.11288; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Praneeth Narisetty; Shiva Nagendra Babu Kore","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"13 pages, 10 figures, 8 tables","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11288","date_added":"2026-07-15"},{"row_id":"ale-0142","title":"How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study","url":"https://arxiv.org/abs/2607.10856","canonical_url":"https://arxiv.org/abs/2607.10856","annotation":"Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","key_contribution":"Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","novelty":"Verification is promoted from a final check to a loop-control signal. Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","impact":"Use How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.10856; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yunbo Lyu; David Williams; Jieke Shi; Zhensu Sun; Chao Peng; Zhou Yang; Federica Sarro; David Lo","publication_date":"2026-07-12","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.10856","date_added":"2026-07-15"},{"row_id":"ale-0143","title":"Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries","url":"https://arxiv.org/abs/2607.10113","canonical_url":"https://openreview.net/forum?id=cjU3YbcRr8","annotation":"Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","key_contribution":"Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","impact":"Use Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.10113; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Yubo Li","publication_date":"2026","publication_year":"2026","publication_venue":"Transactions on Machine Learning Research (TMLR)","publisher":"OpenReview","doi":"","publication_note":"Accepted at Transactions on Machine Learning Research (TMLR); the linked arXiv record is the available paper version.","primary_category":"cs.AI","metadata_source":"Current arXiv acceptance note and OpenReview record","github_repo":"","github_stars":"","arxiv_id":"2607.10113","date_added":"2026-07-15"},{"row_id":"ale-0144","title":"Building Effective Agents","url":"https://www.anthropic.com/engineering/building-effective-agents","canonical_url":"https://www.anthropic.com/engineering/building-effective-agents","annotation":"Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","key_contribution":"Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","novelty":"Orchestration and control flow are made explicit and inspectable. Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","impact":"Use Building Effective Agents to turn a recurring-agent idea into an explicit loop contract.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Agent Workflow Patterns","section_slug":"agent-workflow-patterns","lifecycle_stages":"delegation","audience":"builder","evidence_class":"technical-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0145","title":"Harness Engineering for Language Agents: The Harness Layer as Control, Agency, and Runtime","url":"https://www.preprints.org/manuscript/202603.1756","canonical_url":"https://www.preprints.org/manuscript/202603.1756","annotation":"Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","key_contribution":"Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","novelty":"Distills reusable agent-control patterns that are not tied to a single vendor implementation. 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Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops.","impact":"Use How we built our multi-agent research system to turn a recurring-agent idea into an explicit loop contract.","signal":"Contextual source from www.anthropic.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Agent Workflow Patterns","section_slug":"agent-workflow-patterns","lifecycle_stages":"context;delegation","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0147","title":"Building Effective AI Agents: Architecture Patterns and Implementation Frameworks","url":"https://resources.anthropic.com/hubfs/Building%20Effective%20AI%20Agents-%20Architecture%20Patterns%20and%20Implementation%20Frameworks.pdf","canonical_url":"https://resources.anthropic.com/hubfs/Building%20Effective%20AI%20Agents-%20Architecture%20Patterns%20and%20Implementation%20Frameworks.pdf","annotation":"PDF overview of agent architecture patterns, including generator-evaluator loops.","key_contribution":"PDF overview of agent architecture patterns, including generator-evaluator loops.","novelty":"Distills reusable agent-control patterns that are not tied to a single vendor implementation. 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System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies.","impact":"Use AI Agent Architectures to turn a recurring-agent idea into an explicit loop contract.","signal":"Contextual source from hld.handbook.academy; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Agent Workflow Patterns","section_slug":"agent-workflow-patterns","lifecycle_stages":"workspace;context","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"The HLD Handbook","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0149","title":"What Are Agentic Workflows?","url":"https://weaviate.io/blog/what-are-agentic-workflows","canonical_url":"https://weaviate.io/blog/what-are-agentic-workflows","annotation":"Accessible taxonomy of planning, tool use, reflection, and memory patterns.","key_contribution":"Accessible taxonomy of planning, tool use, reflection, and memory patterns.","novelty":"Persistent memory is treated as an external runtime artifact. 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Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior.","impact":"Use 12 Factor Agents to turn a recurring-agent idea into an explicit loop contract.","signal":"Inspectable GitHub source (24,367 stars; 1,848 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Agent Workflow Patterns","section_slug":"agent-workflow-patterns","lifecycle_stages":"state","audience":"builder","evidence_class":"operational-pattern","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-03-30","publication_year":"2025","publication_venue":"humanlayer/12-factor-agents","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"humanlayer/12-factor-agents","github_stars":"24367","arxiv_id":"","date_added":""},{"row_id":"ale-0153","title":"Durable Execution for Agentic Workflows","url":"https://arizenai.com/durable-execution/","canonical_url":"https://arizenai.com/durable-execution/","annotation":"Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows.","key_contribution":"Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. 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Hassan; Hao Li; Dayi Lin; Bram Adams; Tse-Hsun Chen; Yutaro Kashiwa; Dong Qiu","publication_date":"2025-09-07","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2509.06216","date_added":""},{"row_id":"ale-0157","title":"The Art of Loop Engineering","url":"https://www.langchain.com/blog/the-art-of-loop-engineering","canonical_url":"https://www.langchain.com/blog/the-art-of-loop-engineering","annotation":"LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.","key_contribution":"LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.","novelty":"Verification is promoted from a final check to a loop-control signal. 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Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public).","impact":"Use 2026 Agentic Coding Trends Report to turn a recurring-agent idea into an explicit loop contract.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Agent Workflow Patterns","section_slug":"agent-workflow-patterns","lifecycle_stages":"delegation;verification","audience":"builder","evidence_class":"technical-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"url-date","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0161","title":"HomeRail","url":"https://github.com/xiaotianfotos/homerail","canonical_url":"https://github.com/xiaotianfotos/homerail","annotation":"TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.","key_contribution":"TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.","impact":"Use HomeRail to turn a recurring-agent idea into an explicit loop contract.","signal":"Inspectable GitHub source (602 stars; 135 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Agent Workflow Patterns","section_slug":"agent-workflow-patterns","lifecycle_stages":"delegation;verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"xiaotianfotos/homerail","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"xiaotianfotos/homerail","github_stars":"602","arxiv_id":"","date_added":""},{"row_id":"ale-0162","title":"Old and New Apps, via Modern Coding Agents","url":"https://terrytao.wordpress.com/2026/07/11/old-and-new-apps-via-modern-coding-agents/","canonical_url":"https://terrytao.wordpress.com/2026/07/11/old-and-new-apps-via-modern-coding-agents/","annotation":"Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was \"a net wash\" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","key_contribution":"Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was \"a net wash\" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","novelty":"Verification is promoted from a final check to a loop-control signal. Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was \"a net wash\" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","impact":"Use Old and New Apps, via Modern Coding Agents to turn a recurring-agent idea into an explicit loop contract.","signal":"Contextual source from terrytao.wordpress.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Agent Workflow Patterns","section_slug":"agent-workflow-patterns","lifecycle_stages":"verification;escalation","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-11","publication_year":"2026","publication_venue":"","publisher":"What's new","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0163","title":"Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable","url":"https://arxiv.org/abs/2607.13285","canonical_url":"https://arxiv.org/abs/2607.13285","annotation":"Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use.","key_contribution":"Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use.","novelty":"Distills reusable agent-control patterns that are not tied to a single vendor implementation. 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Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions.","impact":"Use MemoHarness: Agent Harnesses That Learn from Experience to turn a recurring-agent idea into an explicit loop contract.","signal":"Research source arXiv:2607.14159; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Agent Workflow Patterns","section_slug":"agent-workflow-patterns","lifecycle_stages":"context","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yue Huang; Wenjie Wang; Han Bao; Yuchen Ma; Xiaonan Luo; Yi Nian; Haomin Zhuang; Zheyuan Liu; Yue Zhao; Xiangliang Zhang","publication_date":"2026-07-14","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14159","date_added":"2026-07-17"},{"row_id":"ale-0165","title":"SWE-agent","url":"https://github.com/SWE-agent/SWE-agent","canonical_url":"https://github.com/SWE-agent/SWE-agent","annotation":"Agent-computer interface and autonomous software engineering agent for repository tasks.","key_contribution":"Agent-computer interface and autonomous software engineering agent for repository tasks.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Agent-computer interface and autonomous software engineering agent for repository tasks.","impact":"Use SWE-agent to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (19,839 stars; 2,167 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2024-04-02","publication_year":"2024","publication_venue":"SWE-agent/SWE-agent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"SWE-agent/SWE-agent","github_stars":"19839","arxiv_id":"","date_added":""},{"row_id":"ale-0166","title":"SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering","url":"https://arxiv.org/abs/2405.15793","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2024/hash/5a7c947568c1b1328ccc5230172e1e7c-Abstract-Conference.html","annotation":"Paper behind SWE-agent and its interface design.","key_contribution":"Paper behind SWE-agent and its interface design.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Paper behind SWE-agent and its interface design.","impact":"Use SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2405.15793; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"John Yang; Carlos E. Jimenez; Alexander Wettig; Kilian Lieret; Shunyu Yao; Karthik Narasimhan; Ofir Press","publication_date":"2024","publication_year":"2024","publication_venue":"Advances in Neural Information Processing Systems 37 (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"10.52202/079017-1601","publication_note":"Published in Advances in Neural Information Processing Systems 37 (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"NeurIPS proceedings and DOI records","github_repo":"","github_stars":"","arxiv_id":"2405.15793","date_added":""},{"row_id":"ale-0167","title":"mini-SWE-agent","url":"https://mini-swe-agent.com/latest/","canonical_url":"https://mini-swe-agent.com/latest/","annotation":"Minimal coding agent that is useful for understanding the core loop without a large framework.","key_contribution":"Minimal coding agent that is useful for understanding the core loop without a large framework.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Minimal coding agent that is useful for understanding the core loop without a large framework.","impact":"Use mini-SWE-agent to choose an implementation surface for repeatable agent work.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"implementation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"mini-swe-agent.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0168","title":"OpenHands","url":"https://github.com/All-Hands-AI/OpenHands","canonical_url":"https://github.com/OpenHands/OpenHands","annotation":"Open platform for AI software developers as generalist agents.","key_contribution":"Open platform for AI software developers as generalist agents.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Open platform for AI software developers as generalist agents.","impact":"Use OpenHands to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (81,100 stars; 10,369 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2024-03-13","publication_year":"2024","publication_venue":"All-Hands-AI/OpenHands","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"All-Hands-AI/OpenHands","github_stars":"81100","arxiv_id":"","date_added":""},{"row_id":"ale-0169","title":"OpenHands: An Open Platform for AI Software Developers as Generalist Agents","url":"https://arxiv.org/abs/2407.16741","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2025/hash/a4b6ad6b48850c0c331d1259fc66a69c-Abstract-Conference.html","annotation":"Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","key_contribution":"Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","impact":"Use OpenHands: An Open Platform for AI Software Developers as Generalist Agents to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2407.16741; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Xingyao Wang; Boxuan Li; Yufan Song; Frank F. Xu; Xiangru Tang; Mingchen Zhuge; Jiayi Pan; Yueqi Song; Bowen Li; Jaskirat Singh; Hoang H. Tran; Fuqiang Li; Ren Ma; Mingzhang Zheng; Bill Qian; Yanjun Shao; Niklas Muennighoff; Yizhe Zhang; Binyuan Hui; Junyang Lin; Robert Brennan; Hao Peng; Heng Ji; Graham Neubig","publication_date":"2025","publication_year":"2025","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2407.16741","date_added":""},{"row_id":"ale-0170","title":"Agentless","url":"https://github.com/OpenAutoCoder/Agentless","canonical_url":"https://github.com/OpenAutoCoder/Agentless","annotation":"Workflow-based approach for software issue resolution using localization, repair, and patch validation.","key_contribution":"Workflow-based approach for software issue resolution using localization, repair, and patch validation.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Workflow-based approach for software issue resolution using localization, repair, and patch validation.","impact":"Use Agentless to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (2,083 stars; 235 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"intake","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2024-06-30","publication_year":"2024","publication_venue":"OpenAutoCoder/Agentless","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"OpenAutoCoder/Agentless","github_stars":"2083","arxiv_id":"","date_added":""},{"row_id":"ale-0171","title":"Agentless: Demystifying LLM-based Software Engineering Agents","url":"https://arxiv.org/abs/2407.01489","canonical_url":"https://arxiv.org/abs/2407.01489","annotation":"Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.","key_contribution":"Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.","impact":"Use Agentless: Demystifying LLM-based Software Engineering Agents to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2407.01489; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Chunqiu Steven Xia; Yinlin Deng; Soren Dunn; Lingming Zhang","publication_date":"2024-07-01","publication_year":"2024","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2407.01489","date_added":""},{"row_id":"ale-0172","title":"AutoCodeRover","url":"https://github.com/AutoCodeRoverSG/auto-code-rover","canonical_url":"https://github.com/AutoCodeRoverSG/auto-code-rover","annotation":"Autonomous program improvement system for issue localization, patch generation, and validation.","key_contribution":"Autonomous program improvement system for issue localization, patch generation, and validation.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Autonomous program improvement system for issue localization, patch generation, and validation.","impact":"Use AutoCodeRover to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (3,096 stars; 334 forks; NOASSERTION license; updated 2026-07-14); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"intake","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2024-04-08","publication_year":"2024","publication_venue":"AutoCodeRoverSG/auto-code-rover","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"AutoCodeRoverSG/auto-code-rover","github_stars":"3096","arxiv_id":"","date_added":""},{"row_id":"ale-0173","title":"AutoCodeRover: Autonomous Program Improvement","url":"https://arxiv.org/abs/2404.05427","canonical_url":"https://doi.org/10.1145/3650212.3680384","annotation":"Paper on autonomous code repair loops over real repositories.","key_contribution":"Paper on autonomous code repair loops over real repositories.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Paper on autonomous code repair loops over real repositories.","impact":"Use AutoCodeRover: Autonomous Program Improvement to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2404.05427; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Yuntong Zhang; Haifeng Ruan; Zhiyu Fan; Abhik Roychoudhury","publication_date":"2024-09-11","publication_year":"2024","publication_venue":"Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)","publisher":"Association for Computing Machinery","doi":"10.1145/3650212.3680384","publication_note":"Published in Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ACM DOI record","github_repo":"","github_stars":"","arxiv_id":"2404.05427","date_added":""},{"row_id":"ale-0174","title":"SWE-bench reading list","url":"https://github.com/SWE-bench/reading-list","canonical_url":"https://github.com/SWE-bench/reading-list","annotation":"Maintained map of software engineering agent systems and related papers.","key_contribution":"Maintained map of software engineering agent systems and related papers.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Maintained map of software engineering agent systems and related papers.","impact":"Use SWE-bench reading list to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (15 stars; 4 forks; updated 2026-06-30); popularity is context, not proof of reliability.","resource_type":"List","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"curated-index","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-06-26","publication_year":"2025","publication_venue":"SWE-bench/reading-list","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"SWE-bench/reading-list","github_stars":"15","arxiv_id":"","date_added":""},{"row_id":"ale-0175","title":"TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code","url":"https://arxiv.org/abs/2602.06875","canonical_url":"https://conf.researchr.org/details/icse-2026/icse-2026-research-track/145/TraceCoder-A-Trace-Driven-Multi-Agent-Framework-for-Automated-Debugging-of-LLM-Gener","annotation":"ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","key_contribution":"ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","impact":"Use TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2602.06875; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;state","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Jiangping Huang; Wenguang Ye; Weisong Sun; Jian Zhang; Mingyue Zhang; Yang Liu","publication_date":"2026-04-12","publication_year":"2026","publication_venue":"Proceedings of the 48th IEEE/ACM International Conference on Software Engineering (ICSE)","publisher":"Association for Computing Machinery","doi":"10.1145/3744916.3773187","publication_note":"Published in Proceedings of the 48th IEEE/ACM International Conference on Software Engineering (ICSE); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ICSE program and camera-ready records","github_repo":"","github_stars":"","arxiv_id":"2602.06875","date_added":""},{"row_id":"ale-0176","title":"The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase","url":"https://arxiv.org/abs/2603.25697","canonical_url":"https://arxiv.org/abs/2603.25697","annotation":"Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","key_contribution":"Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","impact":"Use The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2603.25697; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yannick Roy","publication_date":"2026-03-26","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.25697","date_added":""},{"row_id":"ale-0177","title":"Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures","url":"https://arxiv.org/abs/2604.03515","canonical_url":"https://arxiv.org/abs/2604.03515","annotation":"Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","key_contribution":"Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","novelty":"State persistence is explicit enough for repeated runs and handoff. Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","impact":"Use Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2604.03515; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;verification;state;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Benjamin Rombaut","publication_date":"2026-04-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.03515","date_added":""},{"row_id":"ale-0178","title":"A Self-Improving Coding Agent","url":"https://arxiv.org/abs/2504.15228","canonical_url":"https://arxiv.org/abs/2504.15228","annotation":"An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","key_contribution":"An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","novelty":"Verification is promoted from a final check to a loop-control signal. An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","impact":"Use A Self-Improving Coding Agent to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2504.15228; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Maxime Robeyns; Martin Szummer; Laurence Aitchison","publication_date":"2025-04-21","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Submitted as a preprint to NeurIPS 2025","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2504.15228","date_added":""},{"row_id":"ale-0179","title":"Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality","url":"https://arxiv.org/abs/2607.03691","canonical_url":"https://arxiv.org/abs/2607.03691","annotation":"Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","key_contribution":"Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","impact":"Use Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.03691; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;context","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Oussama Ben Sghaier; Hao Li; Bram Adams; Ahmed E. Hassan","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.03691","date_added":""},{"row_id":"ale-0180","title":"ToFu: A White-Box, Token-Efficient Agent Harness for Researchers","url":"https://arxiv.org/abs/2607.11423","canonical_url":"https://arxiv.org/abs/2607.11423","annotation":"MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","key_contribution":"MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","novelty":"Orchestration and control flow are made explicit and inspectable. MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","impact":"Use ToFu: A White-Box, Token-Efficient Agent Harness for Researchers to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.11423; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Junhao Ruan; Yuan Ge; Bei Li; Yongjing Yin; Yuchun Fan; Xin Chen; Jingang Wang; Chenglong Wang; Jingbo Zhu; Tong Xiao","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11423","date_added":"2026-07-15"},{"row_id":"ale-0181","title":"When Does Restricting a Coding Agent to execute_code Help?","url":"https://arxiv.org/abs/2607.10569","canonical_url":"https://arxiv.org/abs/2607.10569","annotation":"Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","key_contribution":"Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","impact":"Use When Does Restricting a Coding Agent to execute_code Help? to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.10569; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Hong Yang; Qi Yu; Travis Desell","publication_date":"2026","publication_year":"2026","publication_venue":"KDD Workshop on Agentic Software Engineering (SE 3.0)","publisher":"ACM SIGKDD","doi":"","publication_note":"Accepted at KDD Workshop on Agentic Software Engineering (SE 3.0); the linked arXiv record is the available paper version.","primary_category":"cs.SE","metadata_source":"Current arXiv acceptance note and official non-archival workshop page","github_repo":"","github_stars":"","arxiv_id":"2607.10569","date_added":"2026-07-15"},{"row_id":"ale-0182","title":"Ralph","url":"https://ghuntley.com/ralph/","canonical_url":"https://ghuntley.com/ralph/","annotation":"Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.","key_contribution":"Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.","novelty":"Persistent memory is treated as an external runtime artifact. Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.","impact":"Use Ralph to choose an implementation surface for repeatable agent work.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","evidence_class":"operational-pattern","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-07-14","publication_year":"2025","publication_venue":"","publisher":"Geoffrey Huntley","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0183","title":"everything is a ralph loop","url":"https://ghuntley.com/loop/","canonical_url":"https://ghuntley.com/loop/","annotation":"Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","key_contribution":"Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","impact":"Use everything is a ralph loop to choose an implementation surface for repeatable agent work.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context;verification","audience":"builder","evidence_class":"operational-pattern","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-17","publication_year":"2026","publication_venue":"","publisher":"Geoffrey Huntley","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0184","title":"how-to-ralph-wiggum","url":"https://github.com/ghuntley/how-to-ralph-wiggum","canonical_url":"https://github.com/ghuntley/how-to-ralph-wiggum","annotation":"Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","key_contribution":"Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","impact":"Use how-to-ralph-wiggum to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,720 stars; 146 forks; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-10","publication_year":"2026","publication_venue":"ghuntley/how-to-ralph-wiggum","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ghuntley/how-to-ralph-wiggum","github_stars":"1720","arxiv_id":"","date_added":""},{"row_id":"ale-0185","title":"A Brief History of Ralph","url":"https://www.humanlayer.dev/blog/brief-history-of-ralph","canonical_url":"https://www.humanlayer.dev/blog/brief-history-of-ralph","annotation":"Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.","key_contribution":"Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.","impact":"Use A Brief History of Ralph to choose an implementation surface for repeatable agent work.","signal":"Contextual source from www.humanlayer.dev; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026","publication_year":"2026","publication_venue":"","publisher":"humanlayer.dev","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0186","title":"Ralph Copilot","url":"https://github.com/giocaizzi/ralph-copilot/tree/e5b2813cc876c73a8c9d3398c0115da0d15f63cf","canonical_url":"https://github.com/giocaizzi/ralph-copilot/tree/e5b2813cc876c73a8c9d3398c0115da0d15f63cf","annotation":"Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","key_contribution":"Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","novelty":"Persistent memory is treated as an external runtime artifact. Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","impact":"Use Ralph Copilot to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (138 stars; 16 forks; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Pattern","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","evidence_class":"operational-pattern","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-30","publication_year":"2026","publication_venue":"giocaizzi/ralph-copilot","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"giocaizzi/ralph-copilot","github_stars":"138","arxiv_id":"","date_added":""},{"row_id":"ale-0187","title":"Ralph (snarktank)","url":"https://github.com/snarktank/ralph","canonical_url":"https://github.com/snarktank/ralph","annotation":"Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","key_contribution":"Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","novelty":"State persistence is explicit enough for repeated runs and handoff. Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","impact":"Use Ralph (snarktank) to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (21,112 stars; 2,043 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-07","publication_year":"2026","publication_venue":"snarktank/ralph","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"snarktank/ralph","github_stars":"21112","arxiv_id":"","date_added":""},{"row_id":"ale-0188","title":"ralph-claude-code","url":"https://github.com/frankbria/ralph-claude-code","canonical_url":"https://github.com/frankbria/ralph-claude-code","annotation":"Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","key_contribution":"Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","impact":"Use ralph-claude-code to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (9,544 stars; 728 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"exit","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-08-27","publication_year":"2025","publication_venue":"frankbria/ralph-claude-code","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"frankbria/ralph-claude-code","github_stars":"9544","arxiv_id":"","date_added":""},{"row_id":"ale-0189","title":"ralph-orchestrator","url":"https://github.com/mikeyobrien/ralph-orchestrator","canonical_url":"https://github.com/mikeyobrien/ralph-orchestrator","annotation":"Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","key_contribution":"Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","novelty":"Orchestration and control flow are made explicit and inspectable. Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","impact":"Use ralph-orchestrator to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (3,009 stars; 282 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;escalation;exit","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-09-07","publication_year":"2025","publication_venue":"mikeyobrien/ralph-orchestrator","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"mikeyobrien/ralph-orchestrator","github_stars":"3009","arxiv_id":"","date_added":""},{"row_id":"ale-0190","title":"ralphex","url":"https://github.com/umputun/ralphex","canonical_url":"https://github.com/umputun/ralphex","annotation":"Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","key_contribution":"Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","impact":"Use ralphex to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,383 stars; 112 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-19","publication_year":"2026","publication_venue":"umputun/ralphex","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"umputun/ralphex","github_stars":"1383","arxiv_id":"","date_added":""},{"row_id":"ale-0191","title":"ralph (iannuttall)","url":"https://github.com/iannuttall/ralph","canonical_url":"https://github.com/iannuttall/ralph","annotation":"File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","key_contribution":"File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","novelty":"Persistent memory is treated as an external runtime artifact. File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","impact":"Use ralph (iannuttall) to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (932 stars; 91 forks; updated 2026-07-11); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-12","publication_year":"2026","publication_venue":"iannuttall/ralph","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"iannuttall/ralph","github_stars":"932","arxiv_id":"","date_added":""},{"row_id":"ale-0192","title":"ralph-loop-agent","url":"https://github.com/vercel-labs/ralph-loop-agent","canonical_url":"https://github.com/vercel-labs/ralph-loop-agent","annotation":"Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","key_contribution":"Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","novelty":"Verification is promoted from a final check to a loop-control signal. Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","impact":"Use ralph-loop-agent to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (821 stars; 86 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;budget;exit","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-03","publication_year":"2026","publication_venue":"vercel-labs/ralph-loop-agent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"vercel-labs/ralph-loop-agent","github_stars":"821","arxiv_id":"","date_added":""},{"row_id":"ale-0193","title":"Open Ralph Wiggum","url":"https://github.com/Th0rgal/open-ralph-wiggum","canonical_url":"https://github.com/Th0rgal/open-ralph-wiggum","annotation":"Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","key_contribution":"Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","impact":"Use Open Ralph Wiggum to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,842 stars; 142 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-06","publication_year":"2026","publication_venue":"Th0rgal/open-ralph-wiggum","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Th0rgal/open-ralph-wiggum","github_stars":"1842","arxiv_id":"","date_added":""},{"row_id":"ale-0194","title":"Compound Engineering","url":"https://every.to/guides/compound-engineering","canonical_url":"https://every.to/guides/compound-engineering","annotation":"Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","key_contribution":"Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","novelty":"Persistent memory is treated as an external runtime artifact. Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","impact":"Use Compound Engineering to choose an implementation surface for repeatable agent work.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","evidence_class":"operational-pattern","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"every.to","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0195","title":"Gas Town","url":"https://github.com/steveyegge/gastown","canonical_url":"https://github.com/gastownhall/gastown","annotation":"Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","key_contribution":"Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","impact":"Use Gas Town to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (17,073 stars; 1,572 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"intake;delegation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-12-16","publication_year":"2025","publication_venue":"steveyegge/gastown","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"steveyegge/gastown","github_stars":"17073","arxiv_id":"","date_added":""},{"row_id":"ale-0196","title":"Amp","url":"https://ampcode.com/","canonical_url":"https://ampcode.com/","annotation":"Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","key_contribution":"Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","impact":"Use Amp to choose an implementation surface for repeatable agent work.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;context;delegation","audience":"builder","evidence_class":"implementation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"ampcode.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0197","title":"karl","url":"https://github.com/kayoslab/karl","canonical_url":"https://github.com/kayoslab/karl","annotation":"Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","key_contribution":"Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","impact":"Use karl to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (0 stars; 0 forks; MIT license; updated 2026-04-08); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;budget","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-10","publication_year":"2026","publication_venue":"kayoslab/karl","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"kayoslab/karl","github_stars":"0","arxiv_id":"","date_added":""},{"row_id":"ale-0198","title":"joelclaw agent-loop skill","url":"https://github.com/joelhooks/joelclaw/blob/main/skills/agent-loop/SKILL.md","canonical_url":"https://github.com/joelhooks/joelclaw/blob/main/skills/agent-loop/SKILL.md","annotation":"Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.","key_contribution":"Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.","impact":"Use joelclaw agent-loop skill to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (60 stars; 3 forks; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Pattern","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"operational-pattern","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-02-14","publication_year":"2026","publication_venue":"joelhooks/joelclaw","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"joelhooks/joelclaw","github_stars":"60","arxiv_id":"","date_added":""},{"row_id":"ale-0199","title":"ARIS (Auto-Research-In-Sleep)","url":"https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep","canonical_url":"https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep","annotation":"Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","key_contribution":"Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","novelty":"Verification is promoted from a final check to a loop-control signal. Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","impact":"Use ARIS (Auto-Research-In-Sleep) to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (13,529 stars; 1,220 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"intake;verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-10","publication_year":"2026","publication_venue":"wanshuiyin/Auto-claude-code-research-in-sleep","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"wanshuiyin/Auto-claude-code-research-in-sleep","github_stars":"13529","arxiv_id":"","date_added":""},{"row_id":"ale-0200","title":"AutoAgent","url":"https://github.com/kevinrgu/autoagent","canonical_url":"https://github.com/kevinrgu/autoagent","annotation":"Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","key_contribution":"Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","novelty":"The work turns loop quality into a measurable task or score. Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","impact":"Use AutoAgent to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (4,544 stars; 498 forks; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-02","publication_year":"2026","publication_venue":"kevinrgu/autoagent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"kevinrgu/autoagent","github_stars":"4544","arxiv_id":"","date_added":""},{"row_id":"ale-0201","title":"zeroshot","url":"https://github.com/the-open-engine/zeroshot","canonical_url":"https://github.com/the-open-engine/zeroshot","annotation":"CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","key_contribution":"CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","novelty":"Verification is promoted from a final check to a loop-control signal. CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","impact":"Use zeroshot to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,647 stars; 141 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-12-25","publication_year":"2025","publication_venue":"the-open-engine/zeroshot","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"the-open-engine/zeroshot","github_stars":"1647","arxiv_id":"","date_added":""},{"row_id":"ale-0202","title":"Loki Mode","url":"https://github.com/asklokesh/loki-mode","canonical_url":"https://github.com/asklokesh/loki-mode","annotation":"Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","key_contribution":"Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","impact":"Use Loki Mode to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,020 stars; 199 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification;state;exit","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-12-26","publication_year":"2025","publication_venue":"asklokesh/loki-mode","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"asklokesh/loki-mode","github_stars":"1020","arxiv_id":"","date_added":""},{"row_id":"ale-0203","title":"Looper","url":"https://github.com/ksimback/looper","canonical_url":"https://github.com/ksimback/looper","annotation":"Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","key_contribution":"Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","novelty":"Verification is promoted from a final check to a loop-control signal. Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","impact":"Use Looper to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (684 stars; 62 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"objective;verification;budget","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-18","publication_year":"2026","publication_venue":"ksimback/looper","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ksimback/looper","github_stars":"684","arxiv_id":"","date_added":""},{"row_id":"ale-0204","title":"Agent Apprenticeship","url":"https://github.com/Forsy-AI/agent-apprenticeship","canonical_url":"https://github.com/Forsy-AI/agent-apprenticeship","annotation":"Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","key_contribution":"Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","novelty":"Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","impact":"Use Agent Apprenticeship to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,316 stars; 56 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-19","publication_year":"2026","publication_venue":"Forsy-AI/agent-apprenticeship","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Forsy-AI/agent-apprenticeship","github_stars":"1316","arxiv_id":"","date_added":""},{"row_id":"ale-0205","title":"Scholar Loop","url":"https://github.com/renee-jia/scholar-loop","canonical_url":"https://github.com/renee-jia/scholar-loop","annotation":"Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","key_contribution":"Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","impact":"Use Scholar Loop to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (461 stars; 36 forks; MIT license; updated 2026-07-08); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-15","publication_year":"2026","publication_venue":"renee-jia/scholar-loop","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"renee-jia/scholar-loop","github_stars":"461","arxiv_id":"","date_added":""},{"row_id":"ale-0206","title":"loop-engineering (Cobus Greyling)","url":"https://github.com/cobusgreyling/loop-engineering","canonical_url":"https://github.com/cobusgreyling/loop-engineering","annotation":"Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's \"Loop Ready\" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","key_contribution":"Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's \"Loop Ready\" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's \"Loop Ready\" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","impact":"Use loop-engineering (Cobus Greyling) to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (8,272 stars; 1,093 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;workspace;context;state;budget","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-09","publication_year":"2026","publication_venue":"cobusgreyling/loop-engineering","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"cobusgreyling/loop-engineering","github_stars":"8272","arxiv_id":"","date_added":""},{"row_id":"ale-0207","title":"AutoCVE","url":"https://github.com/larlarua/AutoCVE","canonical_url":"https://github.com/larlarua/AutoCVE","annotation":"Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","key_contribution":"Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","novelty":"Verification is promoted from a final check to a loop-control signal. Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","impact":"Use AutoCVE to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,293 stars; 85 forks; AGPL-3.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"intake;delegation;verification;exit","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-15","publication_year":"2026","publication_venue":"larlarua/AutoCVE","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"larlarua/AutoCVE","github_stars":"1293","arxiv_id":"","date_added":""},{"row_id":"ale-0208","title":"LoongFlow (Baidu)","url":"https://github.com/baidu-baige/LoongFlow","canonical_url":"https://github.com/baidu-baige/LoongFlow","annotation":"Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","key_contribution":"Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","novelty":"Persistent memory is treated as an external runtime artifact. Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","impact":"Use LoongFlow (Baidu) to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (452 stars; 52 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-12-31","publication_year":"2025","publication_venue":"baidu-baige/LoongFlow","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"baidu-baige/LoongFlow","github_stars":"452","arxiv_id":"","date_added":""},{"row_id":"ale-0209","title":"cc10x","url":"https://github.com/romiluz13/cc10x","canonical_url":"https://github.com/romiluz13/cc10x","annotation":"Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","key_contribution":"Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","impact":"Use cc10x to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (153 stars; 25 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context;verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-10-22","publication_year":"2025","publication_venue":"romiluz13/cc10x","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"romiluz13/cc10x","github_stars":"153","arxiv_id":"","date_added":""},{"row_id":"ale-0210","title":"RigorLoop","url":"https://github.com/ronikobrosly/RigorLoop","canonical_url":"https://github.com/ronikobrosly/RigorLoop","annotation":"Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.","key_contribution":"Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.","impact":"Use RigorLoop to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (134 stars; 1 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"ronikobrosly/RigorLoop","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ronikobrosly/RigorLoop","github_stars":"134","arxiv_id":"","date_added":""},{"row_id":"ale-0211","title":"Open-Inspect","url":"https://github.com/ColeMurray/background-agents","canonical_url":"https://github.com/ColeMurray/background-agents","annotation":"Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","key_contribution":"Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","novelty":"Execution isolation and permission boundaries are part of the design. Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","impact":"Use Open-Inspect to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (2,492 stars; 356 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-25","publication_year":"2026","publication_venue":"ColeMurray/background-agents","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ColeMurray/background-agents","github_stars":"2492","arxiv_id":"","date_added":""},{"row_id":"ale-0212","title":"T3MP3ST","url":"https://github.com/elder-plinius/T3MP3ST","canonical_url":"https://github.com/elder-plinius/T3MP3ST","annotation":"Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","key_contribution":"Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","impact":"Use T3MP3ST to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (4,883 stars; 1,022 forks; AGPL-3.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"elder-plinius/T3MP3ST","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"elder-plinius/T3MP3ST","github_stars":"4883","arxiv_id":"","date_added":""},{"row_id":"ale-0213","title":"Loom","url":"https://github.com/valkor-ai/loom","canonical_url":"https://github.com/valkor-ai/loom","annotation":"Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","key_contribution":"Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","impact":"Use Loom to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (584 stars; 63 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-09","publication_year":"2026","publication_venue":"valkor-ai/loom","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"valkor-ai/loom","github_stars":"584","arxiv_id":"","date_added":""},{"row_id":"ale-0214","title":"Inferoa","url":"https://github.com/agentic-in/inferoa","canonical_url":"https://github.com/agentic-in/inferoa","annotation":"Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","key_contribution":"Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","impact":"Use Inferoa to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (486 stars; 84 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-08","publication_year":"2026","publication_venue":"agentic-in/inferoa","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"agentic-in/inferoa","github_stars":"486","arxiv_id":"","date_added":""},{"row_id":"ale-0215","title":"PlanWeave","url":"https://github.com/GaosCode/PlanWeave","canonical_url":"https://github.com/GaosCode/PlanWeave","annotation":"File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.","key_contribution":"File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.","impact":"Use PlanWeave to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (221 stars; 13 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-05-24","publication_year":"2026","publication_venue":"GaosCode/PlanWeave","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"GaosCode/PlanWeave","github_stars":"221","arxiv_id":"","date_added":""},{"row_id":"ale-0216","title":"loop.js","url":"https://github.com/loop-js/loop.js","canonical_url":"https://github.com/loop-js/loop.js","annotation":"TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","key_contribution":"TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","novelty":"Verification is promoted from a final check to a loop-control signal. TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","impact":"Use loop.js to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (126 stars; 1 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"objective;budget","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"loop-js/loop.js","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"loop-js/loop.js","github_stars":"126","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0217","title":"ai-trains-ai","url":"https://github.com/Danau5tin/ai-trains-ai","canonical_url":"https://github.com/Danau5tin/ai-trains-ai","annotation":"Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","key_contribution":"Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","impact":"Use ai-trains-ai to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (185 stars; 14 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"Danau5tin/ai-trains-ai","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Danau5tin/ai-trains-ai","github_stars":"185","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0218","title":"Factory 2.0: From Coding Agents to Software Factories","url":"https://factory.ai/news/software-factory","canonical_url":"https://factory.ai/news/software-factory","annotation":"Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","key_contribution":"Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","novelty":"Persistent memory is treated as an external runtime artifact. Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","impact":"Use Factory 2.0: From Coding Agents to Software Factories to choose an implementation surface for repeatable agent work.","signal":"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"objective;context;delegation;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Factory","publication_date":"2026-06-15","publication_year":"2026","publication_venue":"","publisher":"Factory","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0219","title":"Superpowers 6","url":"https://blog.fsck.com/2026/06/15/Superpowers-6/","canonical_url":"https://blog.fsck.com/2026/06/15/Superpowers-6/","annotation":"Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","key_contribution":"Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","impact":"Use Superpowers 6 to choose an implementation surface for repeatable agent work.","signal":"Contextual source from blog.fsck.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;verification;budget","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"Massively Parallel Procrastination","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0220","title":"Introducing Devin Security Swarm","url":"https://cognition.com/blog/introducing-devin-security-swarm","canonical_url":"https://cognition.com/blog/introducing-devin-security-swarm","annotation":"Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","key_contribution":"Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","novelty":"The trigger or cadence is explicit, making the workflow recurring rather than one-off. Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","impact":"Use Introducing Devin Security Swarm to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;intake;workspace;verification","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"","publisher":"cognition.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0221","title":"Towards Self-Driving Codebases","url":"https://cursor.com/blog/self-driving-codebases","canonical_url":"https://cursor.com/blog/self-driving-codebases","annotation":"Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","key_contribution":"Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","impact":"Use Towards Self-Driving Codebases to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Wilson Lin","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0222","title":"Factory: Incident Response Automation","url":"https://factory.ai/news/incident-response","canonical_url":"https://factory.ai/news/incident-response","annotation":"Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","key_contribution":"Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","novelty":"State persistence is explicit enough for repeated runs and handoff. Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","impact":"Use Factory: Incident Response Automation to choose an implementation surface for repeatable agent work.","signal":"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Factory","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"","publisher":"Factory","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0223","title":"A Week-Long Autonomous Voxel Manhattan Build","url":"https://x.com/mattshumer_/status/2075268746315268138","canonical_url":"https://x.com/mattshumer_/status/2075268746315268138","annotation":"Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.","key_contribution":"Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.","impact":"Use A Week-Long Autonomous Voxel Manhattan Build to choose an implementation surface for repeatable agent work.","signal":"Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"","publisher":"X (formerly Twitter)","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0224","title":"Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework","url":"https://arxiv.org/abs/2607.13091","canonical_url":"https://arxiv.org/abs/2607.13091","annotation":"Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","key_contribution":"Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","impact":"Use Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.13091; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Aditya Aggarwal; Nahid Farhady Ghalaty","publication_date":"2026-06-22","publication_year":"2026","publication_venue":"32nd IEEE International Conference on Engineering Technology and Innovation (ICE/ITMC)","publisher":"IEEE","doi":"","publication_note":"Accepted at 32nd IEEE International Conference on Engineering Technology and Innovation (ICE/ITMC); the linked arXiv record is the available paper version.","primary_category":"cs.SE","metadata_source":"Current arXiv acceptance note and official conference page","github_repo":"","github_stars":"","arxiv_id":"2607.13091","date_added":"2026-07-17"},{"row_id":"ale-0225","title":"Why Agentic Systems Must Produce Deterministic Outputs to Scale","url":"https://streamzero.com/blog/posts/deep-dives-tools-technologies-architectures/agentic-patterns/why-agentic-systems-must-produce-deterministic-outputs-to-scale","canonical_url":"https://streamzero.com/blog/posts/deep-dives-tools-technologies-architectures/agentic-patterns/why-agentic-systems-must-produce-deterministic-outputs-to-scale","annotation":"Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","key_contribution":"Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","impact":"Use Why Agentic Systems Must Produce Deterministic Outputs to Scale to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from streamzero.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"streamzero.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0226","title":"Stop Babysitting Your Coding Agent. Give It Backpressure.","url":"https://generativeprogrammer.com/p/stop-babysitting-your-coding-agent","canonical_url":"https://generativeprogrammer.com/p/stop-babysitting-your-coding-agent","annotation":"Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","key_contribution":"Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","impact":"Use Stop Babysitting Your Coding Agent. Give It Backpressure. to measure progress and gate completion with repeatable evidence.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;exit","audience":"builder;evaluator","evidence_class":"operational-pattern","signal_strength":"medium","source_status":"ok","authors":"Bilgin Ibryam","publication_date":"","publication_year":"","publication_venue":"","publisher":"generativeprogrammer.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0227","title":"How to Build a Self-Verification Loop in Claude Code","url":"https://dev.to/shipwithaiio/how-to-build-a-self-verification-loop-in-claude-code-3-layers-20-minutes-m1p","canonical_url":"https://dev.to/shipwithaiio/how-to-build-a-self-verification-loop-in-claude-code-3-layers-20-minutes-m1p","annotation":"Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","key_contribution":"Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","novelty":"The agent workflow includes explicit self-checking or gated completion. Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","impact":"Use How to Build a Self-Verification Loop in Claude Code to measure progress and gate completion with repeatable evidence.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"operational-pattern","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"DEV Community","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0228","title":"Agentic Code Review","url":"https://addyosmani.com/blog/agentic-code-review/","canonical_url":"https://addyosmani.com/blog/agentic-code-review/","annotation":"Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","key_contribution":"Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","novelty":"Verification is promoted from a final check to a loop-control signal. Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","impact":"Use Agentic Code Review to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Addy Osmani","publication_date":"","publication_year":"","publication_venue":"","publisher":"addyosmani.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0229","title":"Using DSPy to Evaluate and Improve Datasette Agent's SQL System Prompts","url":"https://simonwillison.net/2026/Jul/2/dspy-datasette-agent-prompts/","canonical_url":"https://simonwillison.net/2026/Jul/2/dspy-datasette-agent-prompts/","annotation":"Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","key_contribution":"Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","impact":"Use Using DSPy to Evaluate and Improve Datasette Agent's SQL System Prompts to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0230","title":"Agentic coding notes","url":"https://danluu.com/ai-coding/","canonical_url":"https://danluu.com/ai-coding/","annotation":"Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","key_contribution":"Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","novelty":"The work turns loop quality into a measurable task or score. Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","impact":"Use Agentic coding notes to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from danluu.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"danluu.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0231","title":"Understanding Is the New Bottleneck","url":"https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html","canonical_url":"https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html","annotation":"Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","key_contribution":"Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","novelty":"Verification is promoted from a final check to a loop-control signal. Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","impact":"Use Understanding Is the New Bottleneck to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.geoffreylitt.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"geoffreylitt.com","doi":"","publication_note":"","primary_category":"","metadata_source":"url-date","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0232","title":"Verifying Agentic Development at Scale","url":"https://cognition.com/blog/testing-development","canonical_url":"https://cognition.com/blog/testing-development","annotation":"Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","key_contribution":"Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","novelty":"Verification is promoted from a final check to a loop-control signal. Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","impact":"Use Verifying Agentic Development at Scale to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-05-29","publication_year":"2026","publication_venue":"","publisher":"cognition.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0233","title":"Loop Engineering Without Verification Is Just Automation","url":"https://www.sonarsource.com/blog/loop-engineering-without-verification-is-just-automation/","canonical_url":"https://www.sonarsource.com/blog/loop-engineering-without-verification-is-just-automation/","annotation":"Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","key_contribution":"Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","novelty":"Verification is promoted from a final check to a loop-control signal. Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","impact":"Use Loop Engineering Without Verification Is Just Automation to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.sonarsource.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"sonarsource.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0234","title":"Closing the Verification Loop: Observability-Driven Harnesses","url":"https://www.datadoghq.com/blog/ai/harness-first-agents/","canonical_url":"https://www.datadoghq.com/blog/ai/harness-first-agents/","annotation":"Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","key_contribution":"Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","novelty":"Verification is promoted from a final check to a loop-control signal. Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","impact":"Use Closing the Verification Loop: Observability-Driven Harnesses to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.datadoghq.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Alp Keles, Jai Menon, Sesh Nalla, Vyom Shah","publication_date":"2026-03-09","publication_year":"2026","publication_venue":"","publisher":"Datadog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0235","title":"How to build a better agent harness with traces and evals","url":"https://arize.com/blog/improve-ai-agents-traces-evals-harness/","canonical_url":"https://arize.com/blog/improve-ai-agents-traces-evals-harness/","annotation":"Trace-evaluate-debug-refine loop for improving agent behavior from real runs.","key_contribution":"Trace-evaluate-debug-refine loop for improving agent behavior from real runs.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Trace-evaluate-debug-refine loop for improving agent behavior from real runs.","impact":"Use How to build a better agent harness with traces and evals to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from arize.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Aaron Winston","publication_date":"2026-05-29","publication_year":"2026","publication_venue":"","publisher":"Arize AI","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0236","title":"Better Harness: A Recipe for Harness Hill-Climbing with Evals","url":"https://www.langchain.com/blog/better-harness-a-recipe-for-harness-hill-climbing-with-evals","canonical_url":"https://www.langchain.com/blog/better-harness-a-recipe-for-harness-hill-climbing-with-evals","annotation":"LangChain's recipe for using evals as the learning signal for harness improvement.","key_contribution":"LangChain's recipe for using evals as the learning signal for harness improvement.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. LangChain's recipe for using evals as the learning signal for harness improvement.","impact":"Use Better Harness: A Recipe for Harness Hill-Climbing with Evals to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"LangChain","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0237","title":"Improving Deep Agents with harness engineering","url":"https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering","canonical_url":"https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering","annotation":"Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","key_contribution":"Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","novelty":"The agent workflow includes explicit self-checking or gated completion. Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","impact":"Use Improving Deep Agents with harness engineering to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"LangChain","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0238","title":"Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses","url":"https://arxiv.org/abs/2604.25850","canonical_url":"https://arxiv.org/abs/2604.25850","annotation":"Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","key_contribution":"Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","novelty":"Verification is promoted from a final check to a loop-control signal. Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","impact":"Use Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2604.25850; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jiahang Lin; Shichun Liu; Chengjun Pan; Lizhi Lin; Shihan Dou; Zhiheng Xi; Xuanjing Huang; Hang Yan; Zhenhua Han; Tao Gui; Yu-Gang Jiang","publication_date":"2026-04-28","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.25850","date_added":""},{"row_id":"ale-0239","title":"Meta-Harness: End-to-End Optimization of Model Harnesses","url":"https://arxiv.org/abs/2603.28052","canonical_url":"https://arxiv.org/abs/2603.28052","annotation":"Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","key_contribution":"Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","impact":"Use Meta-Harness: End-to-End Optimization of Model Harnesses to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2603.28052; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yoonho Lee; Roshen Nair; Qizheng Zhang; Kangwook Lee; Omar Khattab; Chelsea Finn","publication_date":"2026-03-30","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.28052","date_added":""},{"row_id":"ale-0240","title":"HALO (Hierarchical Agent Loop Optimizer)","url":"https://github.com/context-labs/halo","canonical_url":"https://github.com/context-labs/halo","annotation":"Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","key_contribution":"Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","impact":"Use HALO (Hierarchical Agent Loop Optimizer) to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (1,104 stars; 80 forks; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-21","publication_year":"2026","publication_venue":"context-labs/halo","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"context-labs/halo","github_stars":"1104","arxiv_id":"","date_added":""},{"row_id":"ale-0241","title":"Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions","url":"https://arxiv.org/abs/2607.03935","canonical_url":"https://arxiv.org/abs/2607.03935","annotation":"Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","key_contribution":"Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","impact":"Use Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.03935; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Haochen Luo; Yi Huang; Sichun Luo; Fengyuan Liu; Lei Li; Zefa Hu; Junlan Feng; Qi Liu","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.03935","date_added":""},{"row_id":"ale-0242","title":"auto-harness","url":"https://github.com/neosigmaai/auto-harness","canonical_url":"https://github.com/neosigmaai/auto-harness","annotation":"Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","key_contribution":"Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","impact":"Use auto-harness to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (525 stars; 59 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-03","publication_year":"2026","publication_venue":"neosigmaai/auto-harness","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"neosigmaai/auto-harness","github_stars":"525","arxiv_id":"","date_added":""},{"row_id":"ale-0243","title":"OpenAI agent evals","url":"https://developers.openai.com/api/docs/guides/agent-evals","canonical_url":"https://developers.openai.com/api/docs/guides/agent-evals","annotation":"Evaluation guidance for moving from traces to repeatable grading of agent workflows.","key_contribution":"Evaluation guidance for moving from traces to repeatable grading of agent workflows.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Evaluation guidance for moving from traces to repeatable grading of agent workflows.","impact":"Use OpenAI agent evals to measure progress and gate completion with repeatable evidence.","signal":"Primary official documentation from developers.openai.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"OpenAI Developers","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0244","title":"Promptfoo OpenAI Agents provider","url":"https://www.promptfoo.dev/docs/providers/openai-agents/","canonical_url":"https://www.promptfoo.dev/docs/providers/openai-agents/","annotation":"Testing and assertions for multi-turn agent workflows, tools, state, handoffs, sandboxes, and traces.","key_contribution":"Testing and assertions for multi-turn agent workflows, tools, state, handoffs, sandboxes, and traces.","novelty":"Execution isolation and permission boundaries are part of the design. 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UK AISI evaluation framework with solvers, scorers, sandboxing, tool use, MCP, and log viewing.","impact":"Use Inspect AI to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (2,367 stars; 608 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-11-14","publication_year":"2023","publication_venue":"UKGovernmentBEIS/inspect_ai","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"UKGovernmentBEIS/inspect_ai","github_stars":"2367","arxiv_id":"","date_added":""},{"row_id":"ale-0246","title":"OpenTelemetry Semantic Conventions for Generative AI Systems","url":"https://opentelemetry.io/docs/specs/semconv/gen-ai/","canonical_url":"https://opentelemetry.io/docs/specs/semconv/gen-ai/","annotation":"Portable tracing conventions for model calls, tool calls, and agent workflows.","key_contribution":"Portable tracing conventions for model calls, tool calls, and agent workflows.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Portable tracing conventions for model calls, tool calls, and agent workflows.","impact":"Use OpenTelemetry Semantic Conventions for Generative AI Systems to measure progress and gate completion with repeatable evidence.","signal":"Primary official documentation from opentelemetry.io; use it for current product or standard behavior.","resource_type":"Docs","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace","audience":"builder;evaluator","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"OpenTelemetry","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0247","title":"AgentOps","url":"https://github.com/AgentOps-AI/agentops","canonical_url":"https://github.com/AgentOps-AI/agentops","annotation":"Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.","key_contribution":"Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.","impact":"Use AgentOps to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (5,716 stars; 609 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"state;budget","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-08-15","publication_year":"2023","publication_venue":"AgentOps-AI/agentops","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"AgentOps-AI/agentops","github_stars":"5716","arxiv_id":"","date_added":""},{"row_id":"ale-0248","title":"Langfuse","url":"https://github.com/langfuse/langfuse","canonical_url":"https://github.com/langfuse/langfuse","annotation":"Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.","key_contribution":"Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.","impact":"Use Langfuse to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (31,341 stars; 3,307 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-05-18","publication_year":"2023","publication_venue":"langfuse/langfuse","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"langfuse/langfuse","github_stars":"31341","arxiv_id":"","date_added":""},{"row_id":"ale-0249","title":"LangSmith","url":"https://www.langchain.com/langsmith","canonical_url":"https://www.langchain.com/langsmith/observability","annotation":"Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.","key_contribution":"Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.","impact":"Use LangSmith to measure progress and gate completion with repeatable evidence.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"implementation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"LangChain","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0250","title":"Arize Phoenix","url":"https://github.com/Arize-ai/phoenix","canonical_url":"https://github.com/Arize-ai/phoenix","annotation":"Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.","key_contribution":"Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.","impact":"Use Arize Phoenix to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (10,600 stars; 992 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2022-11-09","publication_year":"2022","publication_venue":"Arize-ai/phoenix","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Arize-ai/phoenix","github_stars":"10600","arxiv_id":"","date_added":""},{"row_id":"ale-0251","title":"Braintrust","url":"https://www.braintrust.dev/","canonical_url":"https://www.braintrust.dev/","annotation":"Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","key_contribution":"Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. 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Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.","impact":"Use Weave to measure progress and gate completion with repeatable evidence.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"implementation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Weights & Biases Documentation","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0253","title":"agentops (boshu2)","url":"https://github.com/boshu2/agentops","canonical_url":"https://github.com/boshu2/agentops","annotation":"Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","key_contribution":"Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","novelty":"Verification is promoted from a final check to a loop-control signal. 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CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.","impact":"Use SkillSpec to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (977 stars; 60 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-19","publication_year":"2026","publication_venue":"modiqo/skillspec","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"modiqo/skillspec","github_stars":"977","arxiv_id":"","date_added":""},{"row_id":"ale-0255","title":"Shepherd","url":"https://github.com/shepherd-agents/shepherd","canonical_url":"https://github.com/shepherd-agents/shepherd","annotation":"Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","key_contribution":"Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","impact":"Use Shepherd to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (1,448 stars; 104 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;state","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"shepherd-agents/shepherd","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"shepherd-agents/shepherd","github_stars":"1448","arxiv_id":"","date_added":""},{"row_id":"ale-0256","title":"grill-for-unknowns","url":"https://github.com/nicobailon/grill-for-unknowns","canonical_url":"https://github.com/nicobailon/grill-for-unknowns","annotation":"Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","key_contribution":"Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","novelty":"Verification is promoted from a final check to a loop-control signal. Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","impact":"Use grill-for-unknowns to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (178 stars; 6 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"nicobailon/grill-for-unknowns","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"nicobailon/grill-for-unknowns","github_stars":"178","arxiv_id":"","date_added":""},{"row_id":"ale-0257","title":"Fable Harness","url":"https://github.com/Miguok/fable-harness","canonical_url":"https://github.com/Miguok/fable-harness","annotation":"Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","key_contribution":"Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","impact":"Use Fable Harness to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (190 stars; 33 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;exit","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-05","publication_year":"2026","publication_venue":"Miguok/fable-harness","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Miguok/fable-harness","github_stars":"190","arxiv_id":"","date_added":""},{"row_id":"ale-0258","title":"Mindwalk","url":"https://github.com/cosmtrek/mindwalk","canonical_url":"https://github.com/cosmtrek/mindwalk","annotation":"Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","key_contribution":"Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","impact":"Use Mindwalk to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (771 stars; 45 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"cosmtrek/mindwalk","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"cosmtrek/mindwalk","github_stars":"771","arxiv_id":"","date_added":""},{"row_id":"ale-0259","title":"Waggle","url":"https://github.com/modiqo/waggle","canonical_url":"https://github.com/modiqo/waggle","annotation":"MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","key_contribution":"MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","novelty":"Context is managed as durable loop state rather than a single prompt payload. MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","impact":"Use Waggle to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (808 stars; 141 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"context;delegation;budget","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"modiqo/waggle","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"modiqo/waggle","github_stars":"808","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0260","title":"Jacquard","url":"https://github.com/jbwinters/jacquard-lang","canonical_url":"https://github.com/jbwinters/jacquard-lang","annotation":"Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","key_contribution":"Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","impact":"Use Jacquard to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (93 stars; 2 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"jbwinters/jacquard-lang","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"jbwinters/jacquard-lang","github_stars":"93","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0261","title":"Agentic Verification of Software Systems","url":"https://arxiv.org/abs/2511.17330","canonical_url":"https://doi.org/10.1145/3808164","annotation":"Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","key_contribution":"Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","novelty":"Verification is promoted from a final check to a loop-control signal. Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","impact":"Use Agentic Verification of Software Systems to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2511.17330; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Haoxin Tu; Huan Zhao; Yahui Song; Mehtab Zafar; Ruijie Meng; Abhik Roychoudhury","publication_date":"2026-06-30","publication_year":"2026","publication_venue":"Proceedings of the ACM on Software Engineering 3 (FSE)","publisher":"Association for Computing Machinery","doi":"10.1145/3808164","publication_note":"Published in Proceedings of the ACM on Software Engineering 3 (FSE); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ACM DOI record","github_repo":"","github_stars":"","arxiv_id":"2511.17330","date_added":""},{"row_id":"ale-0262","title":"A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance","url":"https://arxiv.org/abs/2603.18096","canonical_url":"https://doi.org/10.5220/0014840300004015","annotation":"Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","key_contribution":"Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","novelty":"Orchestration and control flow are made explicit and inspectable. Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","impact":"Use A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2603.18096; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Ciprian Paduraru; Petru-Liviu Bouruc; Alin Stefanescu","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 21st International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE)","publisher":"SCITEPRESS","doi":"10.5220/0014840300004015","publication_note":"Published in Proceedings of the 21st International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE); the linked arXiv record remains available for open access.","primary_category":"cs.MA","metadata_source":"SCITEPRESS DOI record","github_repo":"","github_stars":"","arxiv_id":"2603.18096","date_added":""},{"row_id":"ale-0263","title":"Self-Evolving Agents with Anytime-Valid Certificates","url":"https://arxiv.org/abs/2607.00871","canonical_url":"https://arxiv.org/abs/2607.00871","annotation":"Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","key_contribution":"Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","novelty":"Verification is promoted from a final check to a loop-control signal. Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","impact":"Use Self-Evolving Agents with Anytime-Valid Certificates to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.00871; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Biswa Sengupta","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00871","date_added":""},{"row_id":"ale-0264","title":"Delayed Verification Destabilizes Multi-Agent LLM Belief","url":"https://arxiv.org/abs/2606.27409","canonical_url":"https://arxiv.org/abs/2606.27409","annotation":"Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","key_contribution":"Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","novelty":"Verification is promoted from a final check to a loop-control signal. Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","impact":"Use Delayed Verification Destabilizes Multi-Agent LLM Belief to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2606.27409; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Igor Itkin","publication_date":"2026-06-25","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"20 pages, 5 figures, 1 table. Code and data: https://github.com/YehudaItkin/delayed-verification-llm","primary_category":"cs.MA","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.27409","date_added":""},{"row_id":"ale-0265","title":"Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory","url":"https://arxiv.org/abs/2606.06523","canonical_url":"https://arxiv.org/abs/2606.06523","annotation":"Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","key_contribution":"Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","novelty":"Verification is promoted from a final check to a loop-control signal. Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","impact":"Use Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2606.06523; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Ruida Wang; Jerry Huang; Pengcheng Wang; Xuanqing Liu; Luyang Kong; Tong Zhang","publication_date":"2026-06-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.06523","date_added":""},{"row_id":"ale-0266","title":"Regimes: An Auditable, Held-Out-Gated Improvement Loop","url":"https://arxiv.org/abs/2606.10241","canonical_url":"https://arxiv.org/abs/2606.10241","annotation":"Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","key_contribution":"Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","impact":"Use Regimes: An Auditable, Held-Out-Gated Improvement Loop to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2606.10241; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yohei Nakajima","publication_date":"2026-06-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"30 pages, 5 figures. Code and committed runs: https://github.com/yoheinakajima/regimes","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.10241","date_added":""},{"row_id":"ale-0267","title":"Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents","url":"https://arxiv.org/abs/2605.22608","canonical_url":"https://aclanthology.org/2026.acl-demo.74/","annotation":"Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","key_contribution":"Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","impact":"Use Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2605.22608; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Asaf Yehudai; Lilach Eden; Michal Shmueli-Scheuer","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL)","publisher":"Association for Computational Linguistics","doi":"10.18653/v1/2026.acl-demo.74","publication_note":"Published in Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"ACL Anthology and DOI records","github_repo":"","github_stars":"","arxiv_id":"2605.22608","date_added":""},{"row_id":"ale-0268","title":"Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference","url":"https://arxiv.org/abs/2607.02882","canonical_url":"https://arxiv.org/abs/2607.02882","annotation":"FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","key_contribution":"FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","impact":"Use Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.02882; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xuyan Ma; Yawen Wang; Junjie Wang; Xiaofei Xie; Boyu Wu; Mingyang Li; Dandan Wang; Qing Wang","publication_date":"2026-07-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02882","date_added":""},{"row_id":"ale-0269","title":"SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use","url":"https://arxiv.org/abs/2607.01874","canonical_url":"https://arxiv.org/abs/2607.01874","annotation":"Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","key_contribution":"Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","impact":"Use SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.01874; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jiayin Zhu; Kelong Mao; Yudong Guo; Dengbo He; Sulong Xu; Simiu Gu; Yutao Yue","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.01874","date_added":""},{"row_id":"ale-0270","title":"SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Bug Reproduction Tests","url":"https://arxiv.org/abs/2607.00990","canonical_url":"https://arxiv.org/abs/2607.00990","annotation":"Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","key_contribution":"Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","novelty":"Verification is promoted from a final check to a loop-control signal. Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","impact":"Use SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Bug Reproduction Tests to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.00990; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yaoqi Guo; Yang Liu; Jie M. Zhang; Yun Ma; Yiling Lou; Zhenpeng Chen","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00990","date_added":""},{"row_id":"ale-0271","title":"AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation","url":"https://arxiv.org/abs/2607.06273","canonical_url":"https://arxiv.org/abs/2607.06273","annotation":"Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","key_contribution":"Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","impact":"Use AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.06273; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Chenyu Zhao; Shenglin Zhang; Wenwei Gu; Yongqian Sun; Dan Pei; Chetan Bansal; Saravan Rajmohan; Minghua Ma","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06273","date_added":""},{"row_id":"ale-0272","title":"SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review","url":"https://arxiv.org/abs/2607.06065","canonical_url":"https://arxiv.org/abs/2607.06065","annotation":"Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","key_contribution":"Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","novelty":"Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","impact":"Use SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.06065; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Ruoyu Wang; Jierun Chen; Shaowei Wang; Chaofan Tao; Sidi Yang; Yuxin Jiang; Kim-Hui Yap; Lifeng Shang; Xiaohui Li; Haoli Bai","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06065","date_added":""},{"row_id":"ale-0273","title":"Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode","url":"https://arxiv.org/abs/2607.07405","canonical_url":"https://arxiv.org/abs/2607.07405","annotation":"Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","key_contribution":"Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","novelty":"State persistence is explicit enough for repeated runs and handoff. Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","impact":"Use Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07405; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Vikas Reddy; Sumanth Reddy Challaram; Abhishek Basu","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07405","date_added":""},{"row_id":"ale-0274","title":"Harnessing Code Agents for Automatic Software Verification","url":"https://arxiv.org/abs/2607.06341","canonical_url":"https://arxiv.org/abs/2607.06341","annotation":"Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","key_contribution":"Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","novelty":"Verification is promoted from a final check to a loop-control signal. Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","impact":"Use Harnessing Code Agents for Automatic Software Verification to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.06341; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Shuangxiang Kan; Shuanglong Kan; Sebastian Ertel","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.FL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06341","date_added":""},{"row_id":"ale-0275","title":"LLM-as-a-Verifier: A General-Purpose Verification Framework","url":"https://arxiv.org/abs/2607.05391","canonical_url":"https://arxiv.org/abs/2607.05391","annotation":"Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","key_contribution":"Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","novelty":"Verification is promoted from a final check to a loop-control signal. Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","impact":"Use LLM-as-a-Verifier: A General-Purpose Verification Framework to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.05391; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jacky Kwok; Shulu Li; Pranav Atreya; Yuejiang Liu; Yixing Jiang; Chelsea Finn; Marco Pavone; Ion Stoica; Azalia Mirhoseini","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code: https://github.com/llm-as-a-verifier/llm-as-a-verifier Website: https://llm-as-a-verifier.com","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05391","date_added":""},{"row_id":"ale-0276","title":"From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents","url":"https://arxiv.org/abs/2607.08028","canonical_url":"https://arxiv.org/abs/2607.08028","annotation":"Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","key_contribution":"Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","impact":"Use From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.08028; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Joongho Ahn; Moonsoo Kim","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"32 pages, 6 figures, 16 tables. Reference implementation and evaluation artifacts: https://github.com/hammerbaki/enterprise-llm-agent-harness (archived at https://doi.org/10.5281/zenodo.21269426)","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08028","date_added":""},{"row_id":"ale-0277","title":"From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization","url":"https://arxiv.org/abs/2607.07702","canonical_url":"https://arxiv.org/abs/2607.07702","annotation":"STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","key_contribution":"STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","impact":"Use From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07702; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Ying Chang; Jiahang Xu; Xuan Feng; Chenyuan Yang; Peng Cheng; Yuqing Yang","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07702","date_added":""},{"row_id":"ale-0278","title":"Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems","url":"https://arxiv.org/abs/2607.07989","canonical_url":"https://arxiv.org/abs/2607.07989","annotation":"AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","key_contribution":"AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","novelty":"Verification is promoted from a final check to a loop-control signal. AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","impact":"Use Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07989; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Yufei Xia; Anjun Gao; Yueyang Quan; Zhuqing Liu; Minghong Fang","publication_date":"2026","publication_year":"2026","publication_venue":"Conference on Language Modeling (COLM)","publisher":"Conference on Language Modeling","doi":"","publication_note":"Accepted at Conference on Language Modeling (COLM); the linked arXiv record is the available paper version.","primary_category":"cs.CR","metadata_source":"Official COLM accepted-papers list and current arXiv note","github_repo":"","github_stars":"","arxiv_id":"2607.07989","date_added":""},{"row_id":"ale-0279","title":"3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse","url":"https://arxiv.org/abs/2607.07980","canonical_url":"https://arxiv.org/abs/2607.07980","annotation":"Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","key_contribution":"Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","impact":"Use 3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07980; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"context","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Shyam Agarwal; Courtney Miller; Christian Kästner; Bogdan Vasilescu","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07980","date_added":""},{"row_id":"ale-0280","title":"Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring","url":"https://arxiv.org/abs/2607.08066","canonical_url":"https://arxiv.org/abs/2607.08066","annotation":"Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","key_contribution":"Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","impact":"Use Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.08066; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jennifer Za; Julija Bainiaksina; Nikita Ostrovsky; Tanush Chopra; Victoria Krakovna","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"25 pages, 10 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08066","date_added":""},{"row_id":"ale-0281","title":"Physics-Audited Agentic Discovery in Scientific Machine Learning","url":"https://arxiv.org/abs/2607.07379","canonical_url":"https://arxiv.org/abs/2607.07379","annotation":"Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","key_contribution":"Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","novelty":"Verification is promoted from a final check to a loop-control signal. Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","impact":"Use Physics-Audited Agentic Discovery in Scientific Machine Learning to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07379; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Diab W. Abueidda; Bilal Ahmed; Panos Pantidis; Mostafa E. Mobasher","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07379","date_added":""},{"row_id":"ale-0282","title":"Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair","url":"https://arxiv.org/abs/2607.07882","canonical_url":"https://arxiv.org/abs/2607.07882","annotation":"TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","key_contribution":"TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","novelty":"The resource is directly reusable as a starting artifact. TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","impact":"Use Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07882; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"S M Farah Al Fahim; Md Nakhla Rafi; Md Ahasanuzzaman; Zeyang Ma; Dong Jae Kim; Shaowei Wang; Tse-Hsun; Chen","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07882","date_added":""},{"row_id":"ale-0283","title":"Failure as a Process: An Anatomy of CLI Coding Agent Trajectories","url":"https://arxiv.org/abs/2607.09510","canonical_url":"https://arxiv.org/abs/2607.09510","annotation":"Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","key_contribution":"Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","impact":"Use Failure as a Process: An Anatomy of CLI Coding Agent Trajectories to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.09510; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xiangxin Zhao; Han Li; Shuaiting Li; Tianyi Zhao; Earl T. Barr; Federica Sarro; He Ye","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"12 pages, 6 figures","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09510","date_added":""},{"row_id":"ale-0284","title":"Agentic Proof and Property-Based Testing via Property-Templates","url":"https://arxiv.org/abs/2607.09072","canonical_url":"https://arxiv.org/abs/2607.09072","annotation":"Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","key_contribution":"Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","novelty":"Verification is promoted from a final check to a loop-control signal. Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","impact":"Use Agentic Proof and Property-Based Testing via Property-Templates to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.09072; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Seongmin Lee; Yaoxuan Wu; Miryung Kim","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"12 pages, 7 figures, 4 tables; supplementary material included as ancillary file","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09072","date_added":""},{"row_id":"ale-0285","title":"AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP","url":"https://arxiv.org/abs/2607.11098","canonical_url":"https://arxiv.org/abs/2607.11098","annotation":"Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","key_contribution":"Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","impact":"Use AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.11098; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Aritra Mazumder; Nusrat jahan Lia","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11098","date_added":"2026-07-15"},{"row_id":"ale-0286","title":"Latent Programming Horizons in Coding Agents","url":"https://arxiv.org/abs/2607.05188","canonical_url":"https://arxiv.org/abs/2607.05188","annotation":"Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","key_contribution":"Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","novelty":"Verification is promoted from a final check to a loop-control signal. Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","impact":"Use Latent Programming Horizons in Coding Agents to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.05188; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"André Silva; Han Tu; Martin Monperrus","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05188","date_added":"2026-07-15"},{"row_id":"ale-0287","title":"Why evaluate agents","url":"https://adk.dev/evaluate/","canonical_url":"https://adk.dev/evaluate/","annotation":"Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","key_contribution":"Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","novelty":"Primary-source operational guidance rather than commentary. Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","impact":"Use Why evaluate agents to measure progress and gate completion with repeatable evidence.","signal":"Primary official documentation from adk.dev; use it for current product or standard behavior.","resource_type":"Docs","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"Google Agent Development Kit","publication_date":"","publication_year":"","publication_venue":"Google Agent Development Kit","publisher":"Google","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0288","title":"Structured Feedback Improves Repair in an LLM Agent Loop","url":"https://arxiv.org/abs/2607.14167","canonical_url":"https://arxiv.org/abs/2607.14167","annotation":"In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","key_contribution":"In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","novelty":"The contribution is machine-readable and validation-friendly. In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","impact":"Use Structured Feedback Improves Repair in an LLM Agent Loop to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.14167; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jaideep Ray; Ankit Goyal","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14167","date_added":"2026-07-17"},{"row_id":"ale-0289","title":"Copy-on-Write Scoring: Application-Specific Agent Evaluations","url":"https://arxiv.org/abs/2607.14336","canonical_url":"https://arxiv.org/abs/2607.14336","annotation":"Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.","key_contribution":"Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.","novelty":"State persistence is explicit enough for repeated runs and handoff. Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.","impact":"Use Copy-on-Write Scoring: Application-Specific Agent Evaluations to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.14336; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;state","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Joanna Roy; Sven Hoelzel","publication_date":"2026","publication_year":"2026","publication_venue":"ICML Workshop on Agents in the Wild: Safety Security and Beyond","publisher":"International Conference on Machine Learning","doi":"","publication_note":"Accepted at ICML Workshop on Agents in the Wild: Safety Security and Beyond; the linked arXiv record is the available paper version.","primary_category":"cs.SE","metadata_source":"Current arXiv acceptance note and official workshop page","github_repo":"","github_stars":"","arxiv_id":"2607.14336","date_added":"2026-07-17"},{"row_id":"ale-0290","title":"The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK","url":"https://arxiv.org/abs/2607.14340","canonical_url":"https://arxiv.org/abs/2607.14340","annotation":"Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","key_contribution":"Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","novelty":"Verification is promoted from a final check to a loop-control signal. Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","impact":"Use The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.14340; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Tobias Philipp","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14340","date_added":"2026-07-17"},{"row_id":"ale-0291","title":"The lethal trifecta for AI agents","url":"https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/","canonical_url":"https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/","annotation":"Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","key_contribution":"Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","novelty":"Untrusted intake is treated as a loop-level security boundary. Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","impact":"Use The lethal trifecta for AI agents to bound risk before recurring or unattended execution.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"2025","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0292","title":"Prompt injection series","url":"https://simonwillison.net/series/prompt-injection/","canonical_url":"https://simonwillison.net/series/prompt-injection/","annotation":"Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.","key_contribution":"Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.","novelty":"Untrusted intake is treated as a loop-level security boundary. Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.","impact":"Use Prompt injection series to bound risk before recurring or unattended execution.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"intake","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0293","title":"Agentic AI - Threats and Mitigations","url":"https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/","canonical_url":"https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/","annotation":"OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","key_contribution":"OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","novelty":"Persistent memory is treated as an external runtime artifact. OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","impact":"Use Agentic AI - Threats and Mitigations to bound risk before recurring or unattended execution.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"intake;workspace;context;delegation","audience":"builder;operator;security","evidence_class":"technical-documentation","signal_strength":"high","source_status":"ok","authors":"OWASPGenAIProject Editor","publication_date":"","publication_year":"","publication_venue":"","publisher":"OWASP Gen AI Security Project","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0294","title":"Designing AI agents to resist prompt injection","url":"https://openai.com/index/designing-agents-to-resist-prompt-injection/","canonical_url":"https://openai.com/index/designing-agents-to-resist-prompt-injection/","annotation":"OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","key_contribution":"OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","novelty":"Primary-source operational guidance rather than commentary. OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","impact":"Use Designing AI agents to resist prompt injection to bound risk before recurring or unattended execution.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;verification;escalation","audience":"builder;operator;security","evidence_class":"technical-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"OpenAI","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0295","title":"sandbox-runtime","url":"https://github.com/anthropic-experimental/sandbox-runtime","canonical_url":"https://github.com/anthropic-experimental/sandbox-runtime","annotation":"Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.","key_contribution":"Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.","novelty":"Execution isolation and permission boundaries are part of the design. Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.","impact":"Use sandbox-runtime to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (4,694 stars; 364 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-10-20","publication_year":"2025","publication_venue":"anthropic-experimental/sandbox-runtime","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"anthropic-experimental/sandbox-runtime","github_stars":"4694","arxiv_id":"","date_added":""},{"row_id":"ale-0296","title":"E2B","url":"https://github.com/e2b-dev/E2B","canonical_url":"https://github.com/e2b-dev/E2B","annotation":"Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","key_contribution":"Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","novelty":"Execution isolation and permission boundaries are part of the design. Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","impact":"Use E2B to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (13,018 stars; 967 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-03-04","publication_year":"2023","publication_venue":"e2b-dev/E2B","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"e2b-dev/E2B","github_stars":"13018","arxiv_id":"","date_added":""},{"row_id":"ale-0297","title":"Modal Sandboxes","url":"https://modal.com/docs/guide/sandboxes","canonical_url":"https://modal.com/docs/guide/sandboxes","annotation":"Secure sandboxed execution for agent-driven code with resource limits and network controls.","key_contribution":"Secure sandboxed execution for agent-driven code with resource limits and network controls.","novelty":"Execution isolation and permission boundaries are part of the design. Secure sandboxed execution for agent-driven code with resource limits and network controls.","impact":"Use Modal Sandboxes to bound risk before recurring or unattended execution.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","evidence_class":"technical-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Modal","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0298","title":"Daytona","url":"https://www.daytona.io/","canonical_url":"https://www.daytona.io/","annotation":"Infrastructure for running AI-generated code in fast, isolated sandboxes.","key_contribution":"Infrastructure for running AI-generated code in fast, isolated sandboxes.","novelty":"Execution isolation and permission boundaries are part of the design. Infrastructure for running AI-generated code in fast, isolated sandboxes.","impact":"Use Daytona to bound risk before recurring or unattended execution.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","evidence_class":"implementation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"daytona.io","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0299","title":"peerd","url":"https://github.com/NotASithLord/peerd","canonical_url":"https://github.com/NotASithLord/peerd","annotation":"Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","key_contribution":"Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","novelty":"Orchestration and control flow are made explicit and inspectable. Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","impact":"Use peerd to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (360 stars; 35 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;delegation","audience":"builder;operator;security","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-22","publication_year":"2026","publication_venue":"NotASithLord/peerd","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"NotASithLord/peerd","github_stars":"360","arxiv_id":"","date_added":""},{"row_id":"ale-0300","title":"When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents","url":"https://arxiv.org/abs/2607.05189","canonical_url":"https://arxiv.org/abs/2607.05189","annotation":"Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","key_contribution":"Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","impact":"Use When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.05189; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"context;verification;state","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yechao Zhang; Shiqian Zhao; Jiawen Zhang; Jie Zhang; Gelei Deng; Xiaogeng Liu; Chaowei Xiao; Tianwei Zhang","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"25 pages, 8 figures. Preprint","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05189","date_added":""},{"row_id":"ale-0301","title":"Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses","url":"https://arxiv.org/abs/2607.05029","canonical_url":"https://arxiv.org/abs/2607.05029","annotation":"Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","key_contribution":"Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","impact":"Use Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.05029; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"context;verification;state","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Neeraj Karamchandani; Piyush Nagasubramaniam; Sencun Zhu; Dinghao Wu","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Preprint. 10 pages, 2 figures, 4 tables","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05029","date_added":""},{"row_id":"ale-0302","title":"Distributed Attacks in Persistent-State AI Control","url":"https://arxiv.org/abs/2607.02514","canonical_url":"https://arxiv.org/abs/2607.02514","annotation":"Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","key_contribution":"Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","impact":"Use Distributed Attacks in Persistent-State AI Control to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.02514; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"verification;state","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Josh Hills; Ida Caspary; Asa Cooper Stickland","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02514","date_added":""},{"row_id":"ale-0303","title":"ElephantAgent: Contextual State Continuity in Agentic Systems","url":"https://arxiv.org/abs/2607.01919","canonical_url":"https://arxiv.org/abs/2607.01919","annotation":"Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","key_contribution":"Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","novelty":"Verification is promoted from a final check to a loop-control signal. Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","impact":"Use ElephantAgent: Contextual State Continuity in Agentic Systems to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.01919; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context;verification;state","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jiankai Jin; Xiangzheng Zhang; Zhao Liu; Wenzhuo Xu; Dongdong Yang; Deyue Zhang; Quanchen Zou","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.01919","date_added":""},{"row_id":"ale-0304","title":"Cloudflare security-audit-skill","url":"https://github.com/cloudflare/security-audit-skill","canonical_url":"https://github.com/cloudflare/security-audit-skill","annotation":"Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","key_contribution":"Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","novelty":"The contribution is machine-readable and validation-friendly. Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","impact":"Use Cloudflare security-audit-skill to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (2,552 stars; 190 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"verification","audience":"builder;operator;security","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-18","publication_year":"2026","publication_venue":"cloudflare/security-audit-skill","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"cloudflare/security-audit-skill","github_stars":"2552","arxiv_id":"","date_added":""},{"row_id":"ale-0305","title":"The Balkanization of Execution-Security Research for AI Coding Agents","url":"https://arxiv.org/abs/2607.05743","canonical_url":"https://arxiv.org/abs/2607.05743","annotation":"Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","key_contribution":"Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","novelty":"Verification is promoted from a final check to a loop-control signal. Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","impact":"Use The Balkanization of Execution-Security Research for AI Coding Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.05743; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Mohammadreza Rashidi","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"18 pages, 15 figures, 6 tables. Systematizes 39 execution-security papers (2023-2026) into 17 verified categories. Machine-readable corpus and verification script released as a supplementary artifact","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05743","date_added":""},{"row_id":"ale-0306","title":"Context-to-Execution Integrity for LLM Agents","url":"https://arxiv.org/abs/2607.06000","canonical_url":"https://arxiv.org/abs/2607.06000","annotation":"Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","key_contribution":"Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","impact":"Use Context-to-Execution Integrity for LLM Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.06000; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Igor Santos-Grueiro","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"20 pages","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06000","date_added":""},{"row_id":"ale-0307","title":"When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents","url":"https://arxiv.org/abs/2607.06595","canonical_url":"https://arxiv.org/abs/2607.06595","annotation":"GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.","key_contribution":"GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.","novelty":"Persistent memory is treated as an external runtime artifact. GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.","impact":"Use When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.06595; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context;state","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"George Torres; Sharad Shrestha; Satyajayant Misra","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06595","date_added":""},{"row_id":"ale-0308","title":"Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents","url":"https://arxiv.org/abs/2607.08395","canonical_url":"https://arxiv.org/abs/2607.08395","annotation":"Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","key_contribution":"Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","novelty":"Persistent memory is treated as an external runtime artifact. Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","impact":"Use Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.08395; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context;state;budget","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Puji Wang; Yingchen Zhang; Ruqing Zhang; Jiafeng Guo; Xueqi Cheng","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08395","date_added":""},{"row_id":"ale-0309","title":"Prismata: Confining Cross-Site Prompt Injection in Web Agents","url":"https://arxiv.org/abs/2607.08147","canonical_url":"https://arxiv.org/abs/2607.08147","annotation":"Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","key_contribution":"Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","novelty":"Untrusted intake is treated as a loop-level security boundary. Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","impact":"Use Prismata: Confining Cross-Site Prompt Injection in Web Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.08147; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Corban Villa; Alp Eren Ozdarendeli; Sijun Tan; Raluca Ada Popa","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08147","date_added":""},{"row_id":"ale-0310","title":"TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories","url":"https://arxiv.org/abs/2607.08400","canonical_url":"https://arxiv.org/abs/2607.08400","annotation":"Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","key_contribution":"Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","novelty":"The work targets tasks that exceed a single context window or prompt session. Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","impact":"Use TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.08400; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Zheng Gao; Xiaoyu Li; Xiaoyan Feng; Jiaojiao Jiang; Yang Song; Yulei Sui; Zhenchang Xing; Liming Zhu","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08400","date_added":""},{"row_id":"ale-0311","title":"Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents","url":"https://arxiv.org/abs/2607.07474","canonical_url":"https://arxiv.org/abs/2607.07474","annotation":"Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","key_contribution":"Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","impact":"Use Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07474; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Harry Owiredu-Ashley","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"8 pages, 6 figures. Code and artifacts: https://github.com/Harry-Ashley/action-graded-severity","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07474","date_added":""},{"row_id":"ale-0312","title":"Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting","url":"https://arxiv.org/abs/2607.07433","canonical_url":"https://arxiv.org/abs/2607.07433","annotation":"Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","key_contribution":"Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","impact":"Use Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07433; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Aya Spira; Stav Cohen; Elad Feldman; Ron Bitton; Avishai Wool; Ben Nassi","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Website: https://sites.google.com/view/agentic-botnets/home","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07433","date_added":""},{"row_id":"ale-0313","title":"GitLost: How We Tricked GitHub's AI Agent into Leaking Private Repos","url":"https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/","canonical_url":"https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/","annotation":"Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","key_contribution":"Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","impact":"Use GitLost: How We Tricked GitHub's AI Agent into Leaking Private Repos to bound risk before recurring or unattended execution.","signal":"Contextual source from noma.security; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"trigger;intake","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"noma.security","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0314","title":"ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents","url":"https://arxiv.org/abs/2607.07774","canonical_url":"https://arxiv.org/abs/2607.07774","annotation":"Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","key_contribution":"Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","novelty":"The work turns loop quality into a measurable task or score. Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","impact":"Use ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents to bound risk before recurring or unattended execution.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;verification;budget","audience":"researcher;evaluator;operator;security","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Shane Caldwell; Max Harley; Ads Dawson; Michael Kouremetis; Vincent Abruzzo; Will Pearce","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"22 pages, 4 figures, 4 tables","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07774","date_added":""},{"row_id":"ale-0315","title":"Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors","url":"https://arxiv.org/abs/2607.07368","canonical_url":"https://arxiv.org/abs/2607.07368","annotation":"Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","key_contribution":"Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","impact":"Use Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07368; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"objective;delegation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Oliver Makins; Orazio Angelini; Zohreh Shams; Mary Phuong","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Submitted to NeurIPS; 81 pages; 32 figures and 24 tables","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07368","date_added":""},{"row_id":"ale-0316","title":"Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions","url":"https://arxiv.org/abs/2607.07461","canonical_url":"https://arxiv.org/abs/2607.07461","annotation":"Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","key_contribution":"Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","impact":"Use Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07461; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yang Shi; Jiaheng Fu; Yihe Huang; Ruixiang Wu; Chengyao Sun; Kaifeng Huang","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07461","date_added":""},{"row_id":"ale-0317","title":"Factory Droid Shield 2.0: Learned Secret Detection for Autonomous Commits","url":"https://factory.ai/news/droid-shield-2-0","canonical_url":"https://factory.ai/news/droid-shield-2-0","annotation":"Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","key_contribution":"Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","novelty":"Verification is promoted from a final check to a loop-control signal. Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","impact":"Use Factory Droid Shield 2.0: Learned Secret Detection for Autonomous Commits to bound risk before recurring or unattended execution.","signal":"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"verification","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Factory","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"","publisher":"Factory","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0318","title":"destructive_command_guard","url":"https://github.com/Dicklesworthstone/destructive_command_guard","canonical_url":"https://github.com/Dicklesworthstone/destructive_command_guard","annotation":"Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","key_contribution":"Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","impact":"Use destructive_command_guard to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (5,072 stars; 191 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"builder;operator;security","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-07","publication_year":"2026","publication_venue":"Dicklesworthstone/destructive_command_guard","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Dicklesworthstone/destructive_command_guard","github_stars":"5072","arxiv_id":"","date_added":""},{"row_id":"ale-0319","title":"Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution","url":"https://ainowinstitute.org/publications/friendly-fire-exploit-brief","canonical_url":"https://ainowinstitute.org/publications/friendly-fire-exploit-brief","annotation":"Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","key_contribution":"Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","novelty":"Untrusted intake is treated as a loop-level security boundary. Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","impact":"Use Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution to bound risk before recurring or unattended execution.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Boyan Milanov","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"","publisher":"AI Now Institute","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0320","title":"How We Contain Claude Across Products","url":"https://www.anthropic.com/engineering/how-we-contain-claude","canonical_url":"https://www.anthropic.com/engineering/how-we-contain-claude","annotation":"Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","key_contribution":"Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","novelty":"Execution isolation and permission boundaries are part of the design. Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","impact":"Use How We Contain Claude Across Products to bound risk before recurring or unattended execution.","signal":"Contextual source from www.anthropic.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0321","title":"Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Scanner Reliability","url":"https://arxiv.org/abs/2607.11086","canonical_url":"https://arxiv.org/abs/2607.11086","annotation":"Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","key_contribution":"Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","impact":"Use Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Scanner Reliability to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.11086; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Pei Chen; Baichao An; Mengying Wu; Binwang Wan; Geng Hong; Jinsong Chen; Xudong Pan; Jiarun Dai; Min Yang","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"18 pages, 11 figures, and 10 tables. This article substantially extends the preliminary 3-page MCPZoo dataset release arXiv:2512.15144. Includes appendices","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11086","date_added":"2026-07-15"},{"row_id":"ale-0322","title":"Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming","url":"https://arxiv.org/abs/2607.11698","canonical_url":"https://arxiv.org/abs/2607.11698","annotation":"Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","key_contribution":"Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","novelty":"Verification is promoted from a final check to a loop-control signal. Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","impact":"Use Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.11698; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"intake;verification","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xutao Mao; Xiang Zheng; Cong Wang","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11698","date_added":"2026-07-15"},{"row_id":"ale-0323","title":"Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents","url":"https://arxiv.org/abs/2607.10487","canonical_url":"https://arxiv.org/abs/2607.10487","annotation":"Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","key_contribution":"Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","impact":"Use Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.10487; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Igor Santos-Grueiro","publication_date":"2026-07-11","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"20 pages","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.10487","date_added":"2026-07-15"},{"row_id":"ale-0324","title":"ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm","url":"https://arxiv.org/abs/2607.10455","canonical_url":"https://openreview.net/forum?id=YqTodSrPPB","annotation":"Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","key_contribution":"Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","impact":"Use ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.10455; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Kefan Song; Yanjun Qi","publication_date":"2026-04-30","publication_year":"2026","publication_venue":"Proceedings of the 43rd International Conference on Machine Learning (ICML)","publisher":"PMLR","doi":"","publication_note":"Accepted at Proceedings of the 43rd International Conference on Machine Learning (ICML); the linked arXiv record is the available paper version.","primary_category":"cs.AI","metadata_source":"ICML OpenReview record","github_repo":"","github_stars":"","arxiv_id":"2607.10455","date_added":"2026-07-15"},{"row_id":"ale-0325","title":"Clawk","url":"https://github.com/clawkwork/clawk","canonical_url":"https://github.com/clawkwork/clawk","annotation":"Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","key_contribution":"Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","novelty":"Execution isolation and permission boundaries are part of the design. Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","impact":"Use Clawk to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (685 stars; 20 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"clawkwork/clawk","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"clawkwork/clawk","github_stars":"685","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0326","title":"Auto-Review of Agent Actions Without Synchronous Human Oversight","url":"https://alignment.openai.com/auto-review/","canonical_url":"https://alignment.openai.com/auto-review/","annotation":"OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","key_contribution":"OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","impact":"Use Auto-Review of Agent Actions Without Synchronous Human Oversight to bound risk before recurring or unattended execution.","signal":"Contextual source from alignment.openai.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"escalation","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Maja Trębacz; Sam Arnesen; Ollie Matthews; Dylan Hurd; Won Park; Owen Lin; Joe Gershenson","publication_date":"2026-04-30","publication_year":"2026","publication_venue":"OpenAI","publisher":"OpenAI","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0327","title":"SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing","url":"https://arxiv.org/abs/2607.13594","canonical_url":"https://arxiv.org/abs/2607.13594","annotation":"Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","key_contribution":"Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","impact":"Use SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.13594; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"context;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Tianyu Chen; Chujia Hu; Wenjie Wang","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13594","date_added":"2026-07-17"},{"row_id":"ale-0328","title":"CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems","url":"https://arxiv.org/abs/2607.13716","canonical_url":"https://arxiv.org/abs/2607.13716","annotation":"Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","key_contribution":"Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","novelty":"Verification is promoted from a final check to a loop-control signal. Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","impact":"Use CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.13716; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;verification;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Zexun Wang","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"35 pages. Working paper on canonical action verification, runtime governance, semantic pattern detection, and approval-bound action receipts","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13716","date_added":"2026-07-17"},{"row_id":"ale-0329","title":"How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement","url":"https://arxiv.org/abs/2607.13718","canonical_url":"https://arxiv.org/abs/2607.13718","annotation":"Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","key_contribution":"Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","impact":"Use How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.13718; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Alexandra E. Michael; Franziska Roesner","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"15 pages, 4 figures","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13718","date_added":"2026-07-17"},{"row_id":"ale-0330","title":"Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives","url":"https://arxiv.org/abs/2607.14166","canonical_url":"https://arxiv.org/abs/2607.14166","annotation":"Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","key_contribution":"Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","impact":"Use Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.14166; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"verification;escalation;exit","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Sajjad Khan","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"31 pages, 3 figures, 11 tables","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14166","date_added":"2026-07-17"},{"row_id":"ale-0331","title":"Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems","url":"https://arxiv.org/abs/2607.14611","canonical_url":"https://arxiv.org/abs/2607.14611","annotation":"Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","key_contribution":"Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","novelty":"Persistent memory is treated as an external runtime artifact. Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","impact":"Use Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.14611; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"context;state","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Soham Gadgil; David Alexander; Sai Sunku; Franziska Roesner","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Preprint","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14611","date_added":"2026-07-17"},{"row_id":"ale-0332","title":"Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents","url":"https://arxiv.org/abs/2607.15143","canonical_url":"https://arxiv.org/abs/2607.15143","annotation":"Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","key_contribution":"Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","impact":"Use Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.15143; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Aadesh Bagmar; Pushkar Saraf","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.15143","date_added":"2026-07-17"},{"row_id":"ale-0333","title":"Effective Context Engineering for AI Agents","url":"https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents","canonical_url":"https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents","annotation":"Anthropic guide to context as managed runtime state rather than a prompt dump.","key_contribution":"Anthropic guide to context as managed runtime state rather than a prompt dump.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Anthropic guide to context as managed runtime state rather than a prompt dump.","impact":"Use Effective Context Engineering for AI Agents to carry context, state, and receipts across runs and failures.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","evidence_class":"technical-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0334","title":"Agent Harnesses: the Infrastructure Layer Your LLM Agent Actually Needs","url":"https://ninadpathak.com/blog/agent-harnesses/","canonical_url":"https://ninadpathak.com/blog/agent-harnesses/","annotation":"Covers execution loops, state, checkpointing, observers, and replayability.","key_contribution":"Covers execution loops, state, checkpointing, observers, and replayability.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Covers execution loops, state, checkpointing, observers, and replayability.","impact":"Use Agent Harnesses: the Infrastructure Layer Your LLM Agent Actually Needs to carry context, state, and receipts across runs and failures.","signal":"Contextual source from ninadpathak.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"ninadpathak.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0335","title":"The Agent Loop Is the New OS","url":"https://www.harness.io/blog/agent-loop-new-os","canonical_url":"https://www.harness.io/blog/agent-loop-new-os","annotation":"Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.","key_contribution":"Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.","impact":"Use The Agent Loop Is the New OS to carry context, state, and receipts across runs and failures.","signal":"Contextual source from www.harness.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace;context","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026","publication_year":"2026","publication_venue":"","publisher":"Harness.io","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0336","title":"Harness engineering for coding agent users","url":"https://martinfowler.com/articles/harness-engineering.html","canonical_url":"https://martinfowler.com/articles/harness-engineering.html","annotation":"Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","key_contribution":"Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","novelty":"Makes persistence and context management visible as runtime design choices. Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","impact":"Use Harness engineering for coding agent users to carry context, state, and receipts across runs and failures.","signal":"Contextual source from martinfowler.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"martinfowler.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0337","title":"Context Engineering","url":"https://simonwillison.net/2025/Jun/27/context-engineering/","canonical_url":"https://simonwillison.net/2025/Jun/27/context-engineering/","annotation":"Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","key_contribution":"Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","impact":"Use Context Engineering to carry context, state, and receipts across runs and failures.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"2025","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0338","title":"Agentic Coding in 2026","url":"https://sourcegraph.com/blog/agentic-coding","canonical_url":"https://sourcegraph.com/blog/agentic-coding","annotation":"Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","key_contribution":"Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","impact":"Use Agentic Coding in 2026 to carry context, state, and receipts across runs and failures.","signal":"Contextual source from sourcegraph.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Sourcegraph","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0339","title":"Agentic AI State Management with ScyllaDB and LangGraph","url":"https://www.scylladb.com/2026/04/08/agentic-ai-state-management-with-scylladb-and-langgraph/","canonical_url":"https://www.scylladb.com/2026/04/08/agentic-ai-state-management-with-scylladb-and-langgraph/","annotation":"Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","key_contribution":"Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","impact":"Use Agentic AI State Management with ScyllaDB and LangGraph to carry context, state, and receipts across runs and failures.","signal":"Contextual source from www.scylladb.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Cynthia Dunlop","publication_date":"2026-04-08","publication_year":"2026","publication_venue":"","publisher":"ScyllaDB","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0340","title":"Mem0","url":"https://github.com/mem0ai/mem0","canonical_url":"https://github.com/mem0ai/mem0","annotation":"Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","key_contribution":"Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","novelty":"Persistent memory is treated as an external runtime artifact. Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","impact":"Use Mem0 to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (61,068 stars; 7,105 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-06-20","publication_year":"2023","publication_venue":"mem0ai/mem0","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"mem0ai/mem0","github_stars":"61068","arxiv_id":"","date_added":""},{"row_id":"ale-0341","title":"Letta","url":"https://github.com/letta-ai/letta","canonical_url":"https://github.com/letta-ai/letta","annotation":"Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","key_contribution":"Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","novelty":"Persistent memory is treated as an external runtime artifact. Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","impact":"Use Letta to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (23,831 stars; 2,530 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-10-11","publication_year":"2023","publication_venue":"letta-ai/letta","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"letta-ai/letta","github_stars":"23831","arxiv_id":"","date_added":""},{"row_id":"ale-0342","title":"Zep","url":"https://github.com/getzep/zep","canonical_url":"https://github.com/getzep/zep","annotation":"Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.","key_contribution":"Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.","impact":"Use Zep to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (4,762 stars; 641 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-04-29","publication_year":"2023","publication_venue":"getzep/zep","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"getzep/zep","github_stars":"4762","arxiv_id":"","date_added":""},{"row_id":"ale-0343","title":"LangMem","url":"https://github.com/langchain-ai/langmem","canonical_url":"https://github.com/langchain-ai/langmem","annotation":"SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","key_contribution":"SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","novelty":"Persistent memory is treated as an external runtime artifact. SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","impact":"Use LangMem to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (1,566 stars; 177 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-01-21","publication_year":"2025","publication_venue":"langchain-ai/langmem","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"langchain-ai/langmem","github_stars":"1566","arxiv_id":"","date_added":""},{"row_id":"ale-0344","title":"Beads","url":"https://github.com/steveyegge/beads","canonical_url":"https://github.com/gastownhall/beads","annotation":"Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","key_contribution":"Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","impact":"Use Beads to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (25,390 stars; 1,703 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"intake;context;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-10-12","publication_year":"2025","publication_venue":"steveyegge/beads","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"steveyegge/beads","github_stars":"25390","arxiv_id":"","date_added":""},{"row_id":"ale-0345","title":"ARC: Active and Reflection-driven Context Management for Long-Horizon Agents","url":"https://arxiv.org/abs/2601.12030","canonical_url":"https://aclanthology.org/2026.findings-acl.930/","annotation":"Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","key_contribution":"Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","impact":"Use ARC: Active and Reflection-driven Context Management for Long-Horizon Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2601.12030; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Yilun Yao; Shan Huang; Elsie Dai; Zhewen Tan; Zhenyu Duan; Shousheng Jia; Yanbing Jiang; Tong Yang","publication_date":"2026","publication_year":"2026","publication_venue":"Findings of the Association for Computational Linguistics: ACL","publisher":"Association for Computational Linguistics","doi":"10.18653/v1/2026.findings-acl.930","publication_note":"Published in Findings of the Association for Computational Linguistics: ACL; the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ACL Anthology and DOI records","github_repo":"","github_stars":"","arxiv_id":"2601.12030","date_added":""},{"row_id":"ale-0346","title":"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers","url":"https://arxiv.org/abs/2603.07670","canonical_url":"https://arxiv.org/abs/2603.07670","annotation":"Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","key_contribution":"Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","impact":"Use Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2603.07670; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Pengfei Du","publication_date":"2026-03-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.07670","date_added":""},{"row_id":"ale-0347","title":"Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering","url":"https://arxiv.org/abs/2604.08224","canonical_url":"https://arxiv.org/abs/2604.08224","annotation":"Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","key_contribution":"Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","impact":"Use Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2604.08224; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Chenyu Zhou; Huacan Chai; Wenteng Chen; Zihan Guo; Rong Shan; Yuanyi Song; Tianyi Xu; Yingxuan Yang; Aofan Yu; Weiming Zhang; Congming Zheng; Jiachen Zhu; Zeyu Zheng; Zhuosheng Zhang; Xingyu Lou; Changwang Zhang; Zhihui Fu; Jun Wang; Weiwen Liu; Jianghao Lin; Weinan Zhang","publication_date":"2026-04-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"54 pages, tech report on Externalization in LLM Agents","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.08224","date_added":""},{"row_id":"ale-0348","title":"Meta Context Engineering via Agentic Skill Evolution","url":"https://arxiv.org/abs/2601.21557","canonical_url":"https://arxiv.org/abs/2601.21557","annotation":"A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","key_contribution":"A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","novelty":"Context is managed as durable loop state rather than a single prompt payload. A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","impact":"Use Meta Context Engineering via Agentic Skill Evolution to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2601.21557; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Haoran Ye; Xuning He; Vincent Arak; Haonan Dong; Guojie Song","publication_date":"2026-01-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"46 pages, 4 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2601.21557","date_added":""},{"row_id":"ale-0349","title":"Are We Ready for an Agent-Native Memory System?","url":"https://arxiv.org/abs/2606.24775","canonical_url":"https://arxiv.org/abs/2606.24775","annotation":"Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","key_contribution":"Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","novelty":"Persistent memory is treated as an external runtime artifact. Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","impact":"Use Are We Ready for an Agent-Native Memory System? to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2606.24775; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Wei Zhou; Xuanhe Zhou; Shaokun Han; Hongming Xu; Guoliang Li; Zhiyu Li; Feiyu Xiong; Fan Wu","publication_date":"2026-06-23","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Paper list available at: https://github.com/OpenDataBox/awesome-agent-memory. Source code available at: https://github.com/OpenDataBox/MemoryData","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.24775","date_added":""},{"row_id":"ale-0350","title":"Self-Evolving World Models for LLM Agent Planning","url":"https://arxiv.org/abs/2606.30639","canonical_url":"https://arxiv.org/abs/2606.30639","annotation":"Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","key_contribution":"Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","novelty":"Makes persistence and context management visible as runtime design choices. Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","impact":"Use Self-Evolving World Models for LLM Agent Planning to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2606.30639; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xuan Zhang; Wenxuan Zhang; See-Kiong Ng; Yang Deng","publication_date":"2026-06-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.30639","date_added":""},{"row_id":"ale-0351","title":"Rethinking Continual Experience Internalization for Self-Evolving LLM Agents","url":"https://arxiv.org/abs/2606.04703","canonical_url":"https://arxiv.org/abs/2606.04703","annotation":"Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","key_contribution":"Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","novelty":"Makes persistence and context management visible as runtime design choices. Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","impact":"Use Rethinking Continual Experience Internalization for Self-Evolving LLM Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2606.04703; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jingwen Chen; Wenkai Yang; Shengda Fan; Wenbo Nie; Chenxing Sun; Shaodong Zheng; Yangen Hu; Lu Pan; Ke Zeng; Yankai Lin","publication_date":"2026-06-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"10 pages, 8 figures","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.04703","date_added":""},{"row_id":"ale-0352","title":"GenericAgent","url":"https://github.com/lsdefine/GenericAgent","canonical_url":"https://github.com/lsdefine/GenericAgent","annotation":"Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","key_contribution":"Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","novelty":"Persistent memory is treated as an external runtime artifact. Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","impact":"Use GenericAgent to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (13,469 stars; 1,559 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"objective;context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-16","publication_year":"2026","publication_venue":"lsdefine/GenericAgent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"lsdefine/GenericAgent","github_stars":"13469","arxiv_id":"","date_added":""},{"row_id":"ale-0353","title":"Self-GC: Self-Governing Context for Long-Horizon LLM Agents","url":"https://arxiv.org/abs/2607.00692","canonical_url":"https://arxiv.org/abs/2607.00692","annotation":"Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","key_contribution":"Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","impact":"Use Self-GC: Self-Governing Context for Long-Horizon LLM Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.00692; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xubin Hao; Hongjin Meng; Xin Yin; Jiawei Zhu; Chenpeng Cao","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00692","date_added":""},{"row_id":"ale-0354","title":"CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents","url":"https://arxiv.org/abs/2607.05378","canonical_url":"https://arxiv.org/abs/2607.05378","annotation":"Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","key_contribution":"Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","novelty":"Verification is promoted from a final check to a loop-control signal. Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","impact":"Use CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.05378; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yujiang Li; Zhenyu Hou; Yi Jing; Jie Tang; Yuxiao Dong","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05378","date_added":""},{"row_id":"ale-0355","title":"SelfMem: Self-Optimizing Memory for AI Agents","url":"https://arxiv.org/abs/2607.03726","canonical_url":"https://arxiv.org/abs/2607.03726","annotation":"Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","key_contribution":"Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","novelty":"Primary-source operational guidance rather than commentary. Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","impact":"Use SelfMem: Self-Optimizing Memory for AI Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.03726; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Shu Yang; Junchao Wu; Derek F. Wong; Di Wang","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.03726","date_added":""},{"row_id":"ale-0356","title":"Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture","url":"https://arxiv.org/abs/2607.04391","canonical_url":"https://arxiv.org/abs/2607.04391","annotation":"Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","key_contribution":"Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","novelty":"Persistent memory is treated as an external runtime artifact. Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","impact":"Use Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.04391; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Serge Lacasse; Jérémie Hatier; Alex Baker","publication_date":"2026-07-05","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"22 pages, 2 figures","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.04391","date_added":""},{"row_id":"ale-0357","title":"The Log Is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems","url":"https://arxiv.org/abs/2605.21997","canonical_url":"https://arxiv.org/abs/2605.21997","annotation":"BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","key_contribution":"BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","impact":"Use The Log Is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2605.21997; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"objective;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yohei Nakajima","publication_date":"2026-05-21","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"11 pages, 1 figure. Open-source Apache-2.0 implementation with reproducible quickstart demo, deterministic replay, fork-and-diff, and lineage tracing","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.21997","date_added":""},{"row_id":"ale-0358","title":"Agentics: Memorizing Session Transcripts Isn't Useful","url":"https://12gramsofcarbon.com/p/agentics-memorizing-session-transcripts","canonical_url":"https://12gramsofcarbon.com/p/agentics-memorizing-session-transcripts","annotation":"From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","key_contribution":"From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","novelty":"Context is managed as durable loop state rather than a single prompt payload. From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","impact":"Use Agentics: Memorizing Session Transcripts Isn't Useful to carry context, state, and receipts across runs and failures.","signal":"Contextual source from 12gramsofcarbon.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"theahura","publication_date":"","publication_year":"","publication_venue":"","publisher":"12gramsofcarbon.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0359","title":"Long-Running Agents","url":"https://addyo.substack.com/p/long-running-agents","canonical_url":"https://addyo.substack.com/p/long-running-agents","annotation":"Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","key_contribution":"Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","impact":"Use Long-Running Agents to carry context, state, and receipts across runs and failures.","signal":"Contextual source from addyo.substack.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Addy Osmani","publication_date":"","publication_year":"","publication_venue":"","publisher":"Substack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0360","title":"StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems","url":"https://arxiv.org/abs/2607.05844","canonical_url":"https://arxiv.org/abs/2607.05844","annotation":"Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","key_contribution":"Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","novelty":"Persistent memory is treated as an external runtime artifact. Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","impact":"Use StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.05844; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation;state;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Sergey Volkov; Yang Li; Ye Luo","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code and supplementary materials available at: https://github.com/nZiben/statefuse","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05844","date_added":""},{"row_id":"ale-0361","title":"Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents","url":"https://arxiv.org/abs/2607.08716","canonical_url":"https://arxiv.org/abs/2607.08716","annotation":"Names the failure mode \"behavioral state decay\" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","key_contribution":"Names the failure mode \"behavioral state decay\" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","novelty":"Persistent memory is treated as an external runtime artifact. Names the failure mode \"behavioral state decay\" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","impact":"Use Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.08716; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yifan Wu; Lizhu Zhang; Yuhang Zhou; Mingyi Wang; Bo Peng; Serena Li; Xiangjun Fan; Zhuokai Zhao","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08716","date_added":""},{"row_id":"ale-0362","title":"What to Keep, What to Forget: A Rate-Distortion View of Memory Compaction","url":"https://arxiv.org/abs/2607.08032","canonical_url":"https://arxiv.org/abs/2607.08032","annotation":"Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","key_contribution":"Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","novelty":"Persistent memory is treated as an external runtime artifact. Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","impact":"Use What to Keep, What to Forget: A Rate-Distortion View of Memory Compaction to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.08032; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"objective;context;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Ashwin Gerard Colaco; Nada Lahjouji","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08032","date_added":""},{"row_id":"ale-0363","title":"A Hierarchical Memory Architecture Overcomes Context Limits in Long-Horizon Multi-Agent Modeling","url":"https://arxiv.org/abs/2607.07666","canonical_url":"https://arxiv.org/abs/2607.07666","annotation":"Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","key_contribution":"Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","novelty":"Verification is promoted from a final check to a loop-control signal. Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","impact":"Use A Hierarchical Memory Architecture Overcomes Context Limits in Long-Horizon Multi-Agent Modeling to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.07666; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Shivendra G. Tewari; Holly Kimko","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"19 pages, 4 figures, 2 tables. Preprint submitted for publication","primary_category":"q-bio.QM","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07666","date_added":""},{"row_id":"ale-0364","title":"SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents","url":"https://arxiv.org/abs/2607.07676","canonical_url":"https://arxiv.org/abs/2607.07676","annotation":"Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","key_contribution":"Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","novelty":"State persistence is explicit enough for repeated runs and handoff. Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","impact":"Use SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.07676; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Tianming Sha; Yue Zhao; Lichao Sun; Yushun Dong","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"44 pages, 5 figures. Code: https://github.com/LabRAI/SkillCenter ; Data: https://huggingface.co/datasets/Tommysha/skillcenter-bundles","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07676","date_added":""},{"row_id":"ale-0365","title":"How version control will evolve for the agent boom","url":"https://entire.io/blog/how-version-control-will-evolve-for-the-agent-boom","canonical_url":"https://entire.io/blog/how-version-control-will-evolve-for-the-agent-boom","annotation":"Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","key_contribution":"Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","impact":"Use How version control will evolve for the agent boom to carry context, state, and receipts across runs and failures.","signal":"Contextual source from entire.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace;state;exit","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"","publisher":"Entire","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0366","title":"self-learning-skills","url":"https://github.com/Kulaxyz/self-learning-skills","canonical_url":"https://github.com/Kulaxyz/self-learning-skills","annotation":"Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","key_contribution":"Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","impact":"Use self-learning-skills to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (883 stars; 36 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-28","publication_year":"2026","publication_venue":"Kulaxyz/self-learning-skills","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Kulaxyz/self-learning-skills","github_stars":"883","arxiv_id":"","date_added":""},{"row_id":"ale-0367","title":"GitLake: Git-for-data for the agentic lakehouse","url":"https://arxiv.org/abs/2607.08319","canonical_url":"https://arxiv.org/abs/2607.08319","annotation":"Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","key_contribution":"Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","novelty":"Makes persistence and context management visible as runtime design choices. Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","impact":"Use GitLake: Git-for-data for the agentic lakehouse to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.08319; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Weiming Sheng; Jinlang Wang; Manuel Barros; Aldrin Montana; Jacopo Tagliabue; Luca Bigon","publication_date":"2026","publication_year":"2026","publication_venue":"DASHSys Workshop at the International Conference on Very Large Data Bases (VLDB)","publisher":"VLDB Endowment","doi":"","publication_note":"Accepted at DASHSys Workshop at the International Conference on Very Large Data Bases (VLDB); the linked arXiv record is the available paper version.","primary_category":"cs.DB","metadata_source":"Current arXiv acceptance note and official workshop page","github_repo":"","github_stars":"","arxiv_id":"2607.08319","date_added":""},{"row_id":"ale-0368","title":"Shared Selective Persistent Memory for Agentic LLM Systems","url":"https://arxiv.org/abs/2607.09493","canonical_url":"https://arxiv.org/abs/2607.09493","annotation":"Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","key_contribution":"Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","novelty":"Persistent memory is treated as an external runtime artifact. Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","impact":"Use Shared Selective Persistent Memory for Agentic LLM Systems to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.09493; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace;context;state;budget;exit","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Sanjana Pedada; Aditya Dhavala; Neelraj Patil","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"11 pages, 2 figures, 4 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09493","date_added":""},{"row_id":"ale-0369","title":"Scoped Verification for Reliable Long-Horizon Agentic Context Evolution","url":"https://arxiv.org/abs/2607.09175","canonical_url":"https://arxiv.org/abs/2607.09175","annotation":"GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","key_contribution":"GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","impact":"Use Scoped Verification for Reliable Long-Horizon Agentic Context Evolution to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.09175; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Dan C. Hsu; Luke Lu","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"18 pages, 3 figs","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09175","date_added":""},{"row_id":"ale-0370","title":"AgentMemory","url":"https://github.com/rohitg00/agentmemory","canonical_url":"https://github.com/rohitg00/agentmemory","annotation":"Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","key_contribution":"Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","novelty":"Persistent memory is treated as an external runtime artifact. Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","impact":"Use AgentMemory to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (25,285 stars; 2,095 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace;context;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-02-25","publication_year":"2026","publication_venue":"rohitg00/agentmemory","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"rohitg00/agentmemory","github_stars":"25285","arxiv_id":"","date_added":""},{"row_id":"ale-0371","title":"TencentDB-Agent-Memory","url":"https://github.com/TencentCloud/TencentDB-Agent-Memory","canonical_url":"https://github.com/TencentCloud/TencentDB-Agent-Memory","annotation":"Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.","key_contribution":"Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.","novelty":"Persistent memory is treated as an external runtime artifact. Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.","impact":"Use TencentDB-Agent-Memory to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (9,053 stars; 838 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-07","publication_year":"2026","publication_venue":"TencentCloud/TencentDB-Agent-Memory","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"TencentCloud/TencentDB-Agent-Memory","github_stars":"9053","arxiv_id":"","date_added":""},{"row_id":"ale-0372","title":"agent-memory (Neo4j Labs)","url":"https://github.com/neo4j-labs/agent-memory","canonical_url":"https://github.com/neo4j-labs/agent-memory","annotation":"Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","key_contribution":"Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","novelty":"Primary-source operational guidance rather than commentary. Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","impact":"Use agent-memory (Neo4j Labs) to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (374 stars; 86 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-06","publication_year":"2026","publication_venue":"neo4j-labs/agent-memory","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"neo4j-labs/agent-memory","github_stars":"374","arxiv_id":"","date_added":""},{"row_id":"ale-0373","title":"re_gent","url":"https://github.com/regent-vcs/re_gent","canonical_url":"https://github.com/regent-vcs/re_gent","annotation":"Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","key_contribution":"Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","novelty":"Makes persistence and context management visible as runtime design choices. Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","impact":"Use re_gent to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (780 stars; 57 forks; Apache-2.0 license; updated 2026-07-15); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-30","publication_year":"2026","publication_venue":"regent-vcs/re_gent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"regent-vcs/re_gent","github_stars":"780","arxiv_id":"","date_added":""},{"row_id":"ale-0374","title":"StructAgent: Harness Long-Horizon Digital Agents with Unified Causal Structure","url":"https://arxiv.org/abs/2607.11388","canonical_url":"https://arxiv.org/abs/2607.11388","annotation":"State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","key_contribution":"State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","impact":"Use StructAgent: Harness Long-Horizon Digital Agents with Unified Causal Structure to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.11388; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"verification;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Wenyi Wu; Sibo Zhu; Kun Zhou; Aayush Salvi; Zixuan Song; Biwei Huang","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11388","date_added":"2026-07-15"},{"row_id":"ale-0375","title":"The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory","url":"https://arxiv.org/abs/2607.10608","canonical_url":"https://arxiv.org/abs/2607.10608","annotation":"Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","key_contribution":"Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","novelty":"Persistent memory is treated as an external runtime artifact. Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","impact":"Use The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.10608; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yixiong Chen; Xinyi Bai; Alan Yuille","publication_date":"2026-07-12","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.10608","date_added":"2026-07-15"},{"row_id":"ale-0376","title":"Conversational Context: Session, State, and Memory","url":"https://adk.dev/sessions/","canonical_url":"https://adk.dev/sessions/","annotation":"Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","key_contribution":"Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","novelty":"Primary-source operational guidance rather than commentary. Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","impact":"Use Conversational Context: Session, State, and Memory to carry context, state, and receipts across runs and failures.","signal":"Primary official documentation from adk.dev; use it for current product or standard behavior.","resource_type":"Docs","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"Google Agent Development Kit","publication_date":"","publication_year":"","publication_venue":"Google Agent Development Kit","publisher":"Google","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0377","title":"Persistence","url":"https://docs.langchain.com/oss/python/langgraph/persistence","canonical_url":"https://docs.langchain.com/oss/python/langgraph/persistence","annotation":"Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","key_contribution":"Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","novelty":"Primary-source operational guidance rather than commentary. Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","impact":"Use Persistence to carry context, state, and receipts across runs and failures.","signal":"Primary official documentation from docs.langchain.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state;escalation","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"LangChain","publication_date":"","publication_year":"","publication_venue":"LangGraph","publisher":"LangChain","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0378","title":"Workflow checkpoints","url":"https://learn.microsoft.com/en-us/agent-framework/workflows/checkpoints","canonical_url":"https://learn.microsoft.com/en-us/agent-framework/workflows/checkpoints","annotation":"Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","key_contribution":"Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","novelty":"Primary-source operational guidance rather than commentary. Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","impact":"Use Workflow checkpoints to carry context, state, and receipts across runs and failures.","signal":"Primary official documentation from learn.microsoft.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"Microsoft","publication_date":"","publication_year":"","publication_venue":"Microsoft Agent Framework","publisher":"Microsoft","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0379","title":"Agent state","url":"https://strandsagents.com/docs/user-guide/concepts/agents/state/","canonical_url":"https://strandsagents.com/docs/user-guide/concepts/agents/state/","annotation":"Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","key_contribution":"Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","novelty":"Primary-source operational guidance rather than commentary. Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","impact":"Use Agent state to carry context, state, and receipts across runs and failures.","signal":"Primary official documentation from strandsagents.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"Strands Agents","publication_date":"","publication_year":"","publication_venue":"Strands Agents","publisher":"Strands Agents","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0380","title":"Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents","url":"https://arxiv.org/abs/2607.13591","canonical_url":"https://arxiv.org/abs/2607.13591","annotation":"Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","key_contribution":"Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","novelty":"The work turns loop quality into a measurable task or score. Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","impact":"Use Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.13591; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Eric Hanchen Jiang; Zhi Zhang; Yuchen Wu; Levina Li; Dong Liu; Xiao Liang; Rui Sun; Yubei Li; Edward Sun; Haozheng Luo; Zhaolu Kang; Aylin Caliskan; Kai-Wei Chang; Ying Nian Wu","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13591","date_added":"2026-07-17"},{"row_id":"ale-0381","title":"Why Git Is the Memory Solution for the Agentic Development Lifecycle","url":"https://arxiv.org/abs/2607.14390","canonical_url":"https://arxiv.org/abs/2607.14390","annotation":"Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","key_contribution":"Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","novelty":"Persistent memory is treated as an external runtime artifact. Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","impact":"Use Why Git Is the Memory Solution for the Agentic Development Lifecycle to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.14390; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Frank Guo","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"8 pages","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14390","date_added":"2026-07-17"},{"row_id":"ale-0382","title":"AutoGen","url":"https://github.com/microsoft/autogen","canonical_url":"https://github.com/microsoft/autogen","annotation":"Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","key_contribution":"Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","impact":"Use AutoGen to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (59,797 stars; 8,999 forks; CC-BY-4.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;delegation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-08-18","publication_year":"2023","publication_venue":"microsoft/autogen","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"microsoft/autogen","github_stars":"59797","arxiv_id":"","date_added":""},{"row_id":"ale-0383","title":"Microsoft Agent Framework","url":"https://github.com/microsoft/agent-framework","canonical_url":"https://github.com/microsoft/agent-framework","annotation":"Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.","key_contribution":"Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.","impact":"Use Microsoft Agent Framework to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (12,184 stars; 2,047 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-04-28","publication_year":"2025","publication_venue":"microsoft/agent-framework","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"microsoft/agent-framework","github_stars":"12184","arxiv_id":"","date_added":""},{"row_id":"ale-0384","title":"LangGraph","url":"https://github.com/langchain-ai/langgraph","canonical_url":"https://github.com/langchain-ai/langgraph","annotation":"Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","key_contribution":"Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","impact":"Use LangGraph to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (37,514 stars; 6,288 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"state;escalation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-08-09","publication_year":"2023","publication_venue":"langchain-ai/langgraph","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"langchain-ai/langgraph","github_stars":"37514","arxiv_id":"","date_added":""},{"row_id":"ale-0385","title":"CrewAI","url":"https://github.com/crewAIInc/crewAI","canonical_url":"https://github.com/crewAIInc/crewAI","annotation":"Framework for multi-agent workflows organized around roles, tasks, and crews.","key_contribution":"Framework for multi-agent workflows organized around roles, tasks, and crews.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Framework for multi-agent workflows organized around roles, tasks, and crews.","impact":"Use CrewAI to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (55,690 stars; 7,861 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-10-27","publication_year":"2023","publication_venue":"crewAIInc/crewAI","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"crewAIInc/crewAI","github_stars":"55690","arxiv_id":"","date_added":""},{"row_id":"ale-0386","title":"LlamaIndex Workflows","url":"https://developers.llamaindex.ai/python/llamaagents/workflows/","canonical_url":"https://developers.llamaindex.ai/python/llamaagents/workflows/","annotation":"Event-driven workflow abstraction for agentic applications.","key_contribution":"Event-driven workflow abstraction for agentic applications.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. 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API protocol for agent interaction, useful for separating loop managers from agent runtimes.","impact":"Use Agent Protocol to choose an implementation surface for repeatable agent work.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;state","audience":"builder","evidence_class":"technical-documentation","signal_strength":"high","source_status":"ok","authors":"AgentProtocol.ai","publication_date":"","publication_year":"","publication_venue":"","publisher":"AgentProtocol.ai","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0389","title":"AgentKit","url":"https://github.com/inngest/agent-kit","canonical_url":"https://github.com/inngest/agent-kit","annotation":"TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","key_contribution":"TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","impact":"Use AgentKit to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (913 stars; 136 forks; Apache-2.0 license; updated 2026-07-13); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2024-11-18","publication_year":"2024","publication_venue":"inngest/agent-kit","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"inngest/agent-kit","github_stars":"913","arxiv_id":"","date_added":""},{"row_id":"ale-0390","title":"deepagents","url":"https://github.com/langchain-ai/deepagents","canonical_url":"https://github.com/langchain-ai/deepagents","annotation":"LangChain project for deeper, longer-running agents with middleware and harness patterns.","key_contribution":"LangChain project for deeper, longer-running agents with middleware and harness patterns.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. LangChain project for deeper, longer-running agents with middleware and harness patterns.","impact":"Use deepagents to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (26,368 stars; 3,697 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-07-27","publication_year":"2025","publication_venue":"langchain-ai/deepagents","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"langchain-ai/deepagents","github_stars":"26368","arxiv_id":"","date_added":""},{"row_id":"ale-0391","title":"Temporal for AI","url":"https://temporal.io/solutions/ai","canonical_url":"https://temporal.io/solutions/ai","annotation":"Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","key_contribution":"Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","impact":"Use Temporal for AI to choose an implementation surface for repeatable agent work.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"state;budget;escalation","audience":"builder","evidence_class":"technical-documentation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"temporal.io","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0392","title":"Restate","url":"https://restate.dev/","canonical_url":"https://www.restate.dev/","annotation":"Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","key_contribution":"Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","impact":"Use Restate to choose an implementation surface for repeatable agent work.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"state","audience":"builder","evidence_class":"implementation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Restate","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0393","title":"DBOS","url":"https://www.dbos.dev/","canonical_url":"https://www.dbos.dev/","annotation":"Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","key_contribution":"Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","impact":"Use DBOS to choose an implementation surface for repeatable agent work.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;intake","audience":"builder","evidence_class":"implementation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"dbos.dev","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0394","title":"Composio Agent Orchestrator","url":"https://github.com/ComposioHQ/agent-orchestrator","canonical_url":"https://github.com/AgentWrapper/agent-orchestrator","annotation":"Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","key_contribution":"Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","impact":"Use Composio Agent Orchestrator to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (8,332 stars; 1,199 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;delegation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-02-13","publication_year":"2026","publication_venue":"ComposioHQ/agent-orchestrator","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ComposioHQ/agent-orchestrator","github_stars":"8332","arxiv_id":"","date_added":""},{"row_id":"ale-0395","title":"Omnigent","url":"https://github.com/omnigent-ai/omnigent","canonical_url":"https://github.com/omnigent-ai/omnigent","annotation":"Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","key_contribution":"Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","impact":"Use Omnigent to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (7,417 stars; 1,055 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"budget;escalation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-11","publication_year":"2026","publication_venue":"omnigent-ai/omnigent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"omnigent-ai/omnigent","github_stars":"7417","arxiv_id":"","date_added":""},{"row_id":"ale-0396","title":"From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution","url":"https://arxiv.org/abs/2604.11378","canonical_url":"https://arxiv.org/abs/2604.11378","annotation":"Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","key_contribution":"Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","impact":"Use From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2604.11378; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;escalation;exit","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Hu Wei","publication_date":"2026-04-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"51 pages, 4 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.11378","date_added":""},{"row_id":"ale-0397","title":"Eve","url":"https://github.com/vercel/eve","canonical_url":"https://github.com/vercel/eve","annotation":"Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","key_contribution":"Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","impact":"Use Eve to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (3,810 stars; 348 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-16","publication_year":"2026","publication_venue":"vercel/eve","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"vercel/eve","github_stars":"3810","arxiv_id":"","date_added":""},{"row_id":"ale-0398","title":"Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework","url":"https://arxiv.org/abs/2603.11445","canonical_url":"https://openreview.net/forum?id=WUmz4LUbvU","annotation":"Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","key_contribution":"Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","impact":"Use Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2603.11445; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;verification;exit","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Xing Zhang; Yanwei Cui; Guanghui Wang; Wei Qiu; Ziyuan Li; Fangwei Han; Yajing Huang; Hengzhi Qiu; Bing Zhu; Peiyang He","publication_date":"2026","publication_year":"2026","publication_venue":"ICLR Workshop on Multi-Agent Learning: Generalization and Adaptation in Intelligence (MALGAI)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in ICLR Workshop on Multi-Agent Learning: Generalization and Adaptation in Intelligence (MALGAI); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ICLR workshop OpenReview record","github_repo":"","github_stars":"","arxiv_id":"2603.11445","date_added":""},{"row_id":"ale-0399","title":"From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents","url":"https://arxiv.org/abs/2603.22386","canonical_url":"https://arxiv.org/abs/2603.22386","annotation":"Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","key_contribution":"Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","impact":"Use From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2603.22386; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Ling Yue; Kushal Raj Bhandari; Ching-Yun Ko; Dhaval Patel; Shuxin Lin; Nianjun Zhou; Jianxi Gao; Pin-Yu Chen; Shaowu Pan","publication_date":"2026-03-23","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.22386","date_added":""},{"row_id":"ale-0400","title":"Agent-as-a-Router","url":"https://github.com/LanceZPF/agent-as-a-router","canonical_url":"https://github.com/LanceZPF/agent-as-a-router","annotation":"Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","key_contribution":"Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","novelty":"Verification is promoted from a final check to a loop-control signal. Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","impact":"Use Agent-as-a-Router to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (662 stars; 14 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"context;delegation;verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-20","publication_year":"2026","publication_venue":"LanceZPF/agent-as-a-router","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"LanceZPF/agent-as-a-router","github_stars":"662","arxiv_id":"","date_added":""},{"row_id":"ale-0401","title":"Amp: Custom Agents","url":"https://ampcode.com/news/custom-agents","canonical_url":"https://ampcode.com/news/custom-agents","annotation":"Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","key_contribution":"Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","impact":"Use Amp: Custom Agents to choose an implementation surface for repeatable agent work.","signal":"Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;delegation","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"ampcode.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0402","title":"AgentsMesh","url":"https://github.com/AgentsMesh/AgentsMesh","canonical_url":"https://github.com/AgentsMesh/AgentsMesh","annotation":"Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","key_contribution":"Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","impact":"Use AgentsMesh to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (2,282 stars; 228 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;workspace","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-02-28","publication_year":"2026","publication_venue":"AgentsMesh/AgentsMesh","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"AgentsMesh/AgentsMesh","github_stars":"2282","arxiv_id":"","date_added":""},{"row_id":"ale-0403","title":"Bernstein","url":"https://github.com/sipyourdrink-ltd/bernstein","canonical_url":"https://github.com/sipyourdrink-ltd/bernstein","annotation":"Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","key_contribution":"Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","impact":"Use Bernstein to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (688 stars; 62 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;workspace;delegation;verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-22","publication_year":"2026","publication_venue":"sipyourdrink-ltd/bernstein","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"sipyourdrink-ltd/bernstein","github_stars":"688","arxiv_id":"","date_added":""},{"row_id":"ale-0404","title":"Aeon","url":"https://github.com/aaronjmars/aeon","canonical_url":"https://github.com/aeonfun/aeon","annotation":"Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","key_contribution":"Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","novelty":"Persistent memory is treated as an external runtime artifact. Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","impact":"Use Aeon to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (577 stars; 208 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;context;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-04","publication_year":"2026","publication_venue":"aaronjmars/aeon","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"aaronjmars/aeon","github_stars":"577","arxiv_id":"","date_added":""},{"row_id":"ale-0405","title":"h5i","url":"https://github.com/h5i-dev/h5i","canonical_url":"https://github.com/h5i-dev/h5i","annotation":"Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","key_contribution":"Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","impact":"Use h5i to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (470 stars; 39 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-11","publication_year":"2026","publication_venue":"h5i-dev/h5i","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"h5i-dev/h5i","github_stars":"470","arxiv_id":"","date_added":""},{"row_id":"ale-0406","title":"SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery","url":"https://arxiv.org/abs/2607.02807","canonical_url":"https://arxiv.org/abs/2607.02807","annotation":"A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","key_contribution":"A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","novelty":"Context is managed as durable loop state rather than a single prompt payload. A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","impact":"Use SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.02807; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"intake;context","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yuvraj Virk; Zack Edds; Chunqiu Steven Xia; Lingming Zhang","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02807","date_added":""},{"row_id":"ale-0407","title":"Scaling Long-Running Autonomous Coding","url":"https://cursor.com/blog/scaling-agents","canonical_url":"https://cursor.com/blog/scaling-agents","annotation":"Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","key_contribution":"Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","impact":"Use Scaling Long-Running Autonomous Coding to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Wilson Lin","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0408","title":"babysitter","url":"https://github.com/a5c-ai/babysitter","canonical_url":"https://github.com/a5c-ai/babysitter","annotation":"Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","key_contribution":"Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","impact":"Use babysitter to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,556 stars; 90 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;verification;state;escalation;exit","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-05","publication_year":"2026","publication_venue":"a5c-ai/babysitter","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"a5c-ai/babysitter","github_stars":"1556","arxiv_id":"","date_added":""},{"row_id":"ale-0409","title":"claude-code-merge-queue","url":"https://github.com/funador/claude-code-merge-queue","canonical_url":"https://github.com/funador/claude-code-merge-queue","annotation":"Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","key_contribution":"Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","impact":"Use claude-code-merge-queue to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (12 stars; 1 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"intake","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"funador/claude-code-merge-queue","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"funador/claude-code-merge-queue","github_stars":"12","arxiv_id":"","date_added":""},{"row_id":"ale-0410","title":"Devin can now manage Devins","url":"https://cognition.com/blog/devin-can-now-manage-devins","canonical_url":"https://cognition.com/blog/devin-can-now-manage-devins","annotation":"Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","key_contribution":"Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","impact":"Use Devin can now manage Devins to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-03-19","publication_year":"2026","publication_venue":"","publisher":"cognition.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0411","title":"pilotfish","url":"https://github.com/Nanako0129/pilotfish","canonical_url":"https://github.com/Nanako0129/pilotfish","annotation":"Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","key_contribution":"Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","novelty":"Verification is promoted from a final check to a loop-control signal. Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","impact":"Use pilotfish to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (478 stars; 37 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"context;delegation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"Nanako0129/pilotfish","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Nanako0129/pilotfish","github_stars":"478","arxiv_id":"","date_added":""},{"row_id":"ale-0412","title":"fable-advisor","url":"https://github.com/DannyMac180/fable-advisor","canonical_url":"https://github.com/DannyMac180/fable-advisor","annotation":"Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","key_contribution":"Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","novelty":"Verification is promoted from a final check to a loop-control signal. Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","impact":"Use fable-advisor to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (528 stars; 45 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-03","publication_year":"2026","publication_venue":"DannyMac180/fable-advisor","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"DannyMac180/fable-advisor","github_stars":"528","arxiv_id":"","date_added":""},{"row_id":"ale-0413","title":"agent-chief","url":"https://github.com/SmileLikeYe/agent-chief","canonical_url":"https://github.com/SmileLikeYe/agent-chief","annotation":"Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","key_contribution":"Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","impact":"Use agent-chief to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (834 stars; 3 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"escalation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"SmileLikeYe/agent-chief","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"SmileLikeYe/agent-chief","github_stars":"834","arxiv_id":"","date_added":""},{"row_id":"ale-0414","title":"OpenTag","url":"https://github.com/amplifthq/opentag","canonical_url":"https://github.com/amplifthq/opentag","annotation":"Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","key_contribution":"Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","impact":"Use OpenTag to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,363 stars; 73 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"amplifthq/opentag","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"amplifthq/opentag","github_stars":"1363","arxiv_id":"","date_added":""},{"row_id":"ale-0415","title":"herdr","url":"https://github.com/ogulcancelik/herdr","canonical_url":"https://github.com/ogulcancelik/herdr","annotation":"Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","key_contribution":"Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","novelty":"State persistence is explicit enough for repeated runs and handoff. Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","impact":"Use herdr to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (17,542 stars; 1,099 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-27","publication_year":"2026","publication_venue":"ogulcancelik/herdr","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ogulcancelik/herdr","github_stars":"17542","arxiv_id":"","date_added":""},{"row_id":"ale-0416","title":"Orca","url":"https://github.com/stablyai/orca","canonical_url":"https://github.com/stablyai/orca","annotation":"Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","key_contribution":"Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","impact":"Use Orca to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (21,042 stars; 1,523 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-17","publication_year":"2026","publication_venue":"stablyai/orca","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"stablyai/orca","github_stars":"21042","arxiv_id":"","date_added":""},{"row_id":"ale-0417","title":"Agentic Routing: The Harness-Native Data Flywheel","url":"https://arxiv.org/abs/2607.11399","canonical_url":"https://arxiv.org/abs/2607.11399","annotation":"Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","key_contribution":"Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","novelty":"State persistence is explicit enough for repeated runs and handoff. Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","impact":"Use Agentic Routing: The Harness-Native Data Flywheel to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.11399; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"state;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xinchen Liu; Hang Zhou; Yingjie Zong; Yuchuan Tian; Liuyang Song; Shuo Zhang; Yulong Li; Wei He; Mengyu Zheng; Runke Liu; Siyang Cheng; Xiang Kuang; Hailin Hu; Kai Han; Yunhe Wang","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code: https://github.com/opensquilla/opensquilla","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11399","date_added":"2026-07-15"},{"row_id":"ale-0418","title":"A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution","url":"https://arxiv.org/abs/2607.11138","canonical_url":"https://arxiv.org/abs/2607.11138","annotation":"Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","key_contribution":"Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","impact":"Use A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.11138; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"intake;delegation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Prashant Devadiga; Abhishek; Adithya Mishra; Alok Singh; Amisha Sinha; Asit Desai; Gaurang Dahad; Harshit Bhushan; Mandati Pramod Reddy; Prakhar Gupta; Rupesh Patil; Siddhi Behere","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11138","date_added":"2026-07-15"},{"row_id":"ale-0419","title":"Graph-based agent workflows","url":"https://adk.dev/graphs/","canonical_url":"https://adk.dev/graphs/","annotation":"Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","key_contribution":"Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","novelty":"Primary-source operational guidance rather than commentary. Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","impact":"Use Graph-based agent workflows to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from adk.dev; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;state;escalation","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"Google Agent Development Kit","publication_date":"","publication_year":"","publication_venue":"Google Agent Development Kit","publisher":"Google","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0420","title":"Flows","url":"https://docs.crewai.com/en/concepts/flows","canonical_url":"https://docs.crewai.com/v1.15.4/en/concepts/flows","annotation":"Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","key_contribution":"Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","novelty":"Primary-source operational guidance rather than commentary. Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","impact":"Use Flows to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from docs.crewai.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"CrewAI","publication_date":"","publication_year":"","publication_venue":"CrewAI","publisher":"CrewAI","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0421","title":"Graph","url":"https://strandsagents.com/docs/user-guide/concepts/multi-agent/graph/","canonical_url":"https://strandsagents.com/docs/user-guide/concepts/multi-agent/graph/","annotation":"Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","key_contribution":"Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","novelty":"Primary-source operational guidance rather than commentary. Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","impact":"Use Graph to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from strandsagents.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;state","audience":"builder","evidence_class":"official-documentation","signal_strength":"high","source_status":"ok","authors":"Strands Agents","publication_date":"","publication_year":"","publication_venue":"Strands Agents","publisher":"Strands Agents","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0422","title":"SWE-bench","url":"https://www.swebench.com/","canonical_url":"https://www.swebench.com/","annotation":"Benchmark for resolving real GitHub issues through code editing and tests.","key_contribution":"Benchmark for resolving real GitHub issues through code editing and tests.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for resolving real GitHub issues through code editing and tests.","impact":"Use SWE-bench to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"swebench.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0423","title":"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?","url":"https://arxiv.org/abs/2310.06770","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/edac78c3e300629acfe6cbe9ca88fb84-Abstract-Conference.html","annotation":"Original SWE-bench paper.","key_contribution":"Original SWE-bench paper.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Original SWE-bench paper.","impact":"Use SWE-bench: Can Language Models Resolve Real-World GitHub Issues? to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2310.06770; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"intake","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Carlos E. Jimenez; John Yang; Alexander Wettig; Shunyu Yao; Kexin Pei; Ofir Press; Karthik Narasimhan","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2310.06770","date_added":""},{"row_id":"ale-0424","title":"SWE-bench Goes Live","url":"https://arxiv.org/abs/2505.23419","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2025/hash/d83c4a745789690f82e86d0ef752ae7c-Abstract-Datasets_and_Benchmarks_Track.html","annotation":"Dynamic benchmark designed to reduce overfitting to static issue sets.","key_contribution":"Dynamic benchmark designed to reduce overfitting to static issue sets.","novelty":"The work turns loop quality into a measurable task or score. Dynamic benchmark designed to reduce overfitting to static issue sets.","impact":"Use SWE-bench Goes Live to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2505.23419; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Linghao Zhang; Shilin He; Chaoyun Zhang; Yu Kang; Bowen Li; Chengxing Xie; Junhao Wang; Maoquan Wang; Yufan Huang; Shengyu Fu; Elsie Nallipogu; Qingwei Lin; Yingnong Dang; Saravan Rajmohan; Dongmei Zhang","publication_date":"2025","publication_year":"2025","publication_venue":"Advances in Neural Information Processing Systems 38: Datasets and Benchmarks Track (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"","publication_note":"Published in Advances in Neural Information Processing Systems 38: Datasets and Benchmarks Track (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"NeurIPS proceedings record","github_repo":"","github_stars":"","arxiv_id":"2505.23419","date_added":""},{"row_id":"ale-0425","title":"Terminal-Bench","url":"https://www.tbench.ai/","canonical_url":"https://www.tbench.ai/","annotation":"Benchmark for agents operating in terminal environments.","key_contribution":"Benchmark for agents operating in terminal environments.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for agents operating in terminal environments.","impact":"Use Terminal-Bench to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Terminal-Bench","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0426","title":"Terminal-Bench repository","url":"https://github.com/harbor-framework/terminal-bench","canonical_url":"https://github.com/harbor-framework/terminal-bench","annotation":"Open-source benchmark and harness for hard terminal tasks.","key_contribution":"Open-source benchmark and harness for hard terminal tasks.","novelty":"The work turns loop quality into a measurable task or score. Open-source benchmark and harness for hard terminal tasks.","impact":"Use Terminal-Bench repository to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (2,459 stars; 558 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"builder;evaluator","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-01-17","publication_year":"2025","publication_venue":"harbor-framework/terminal-bench","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"harbor-framework/terminal-bench","github_stars":"2459","arxiv_id":"","date_added":""},{"row_id":"ale-0427","title":"AgentBench","url":"https://arxiv.org/abs/2308.03688","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/e9df36b21ff4ee211a8b71ee8b7e9f57-Abstract-Conference.html","annotation":"Multi-environment benchmark for evaluating LLMs as agents.","key_contribution":"Multi-environment benchmark for evaluating LLMs as agents.","novelty":"The work turns loop quality into a measurable task or score. Multi-environment benchmark for evaluating LLMs as agents.","impact":"Use AgentBench to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2308.03688; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Xiao Liu; Hao Yu; Hanchen Zhang; Yifan Xu; Xuanyu Lei; Hanyu Lai; Yu Gu; Hangliang Ding; Kaiwen Men; Kejuan Yang; Shudan Zhang; Xiang Deng; Aohan Zeng; Zhengxiao Du; Chenhui Zhang; Sheng Shen; Tianjun Zhang; Yu Su; Huan Sun; Minlie Huang; Yuxiao Dong; Jie Tang","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2308.03688","date_added":""},{"row_id":"ale-0428","title":"WebArena","url":"https://arxiv.org/abs/2307.13854","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/4410c0711e9154a7a2d26f9b3816d1ef-Abstract-Conference.html","annotation":"Realistic web environment for autonomous agents.","key_contribution":"Realistic web environment for autonomous agents.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Realistic web environment for autonomous agents.","impact":"Use WebArena to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2307.13854; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Shuyan Zhou; Frank F. Xu; Hao Zhu; Xuhui Zhou; Robert Lo; Abishek Sridhar; Xianyi Cheng; Tianyue Ou; Yonatan Bisk; Daniel Fried; Uri Alon; Graham Neubig","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2307.13854","date_added":""},{"row_id":"ale-0429","title":"OSWorld","url":"https://arxiv.org/abs/2404.07972","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2024/hash/5d413e48f84dc61244b6be550f1cd8f5-Abstract-Datasets_and_Benchmarks_Track.html","annotation":"Benchmark for multimodal agents operating full computer environments.","key_contribution":"Benchmark for multimodal agents operating full computer environments.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for multimodal agents operating full computer environments.","impact":"Use OSWorld to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2404.07972; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Tianbao Xie; Danyang Zhang; Jixuan Chen; Xiaochuan Li; Siheng Zhao; Ruisheng Cao; Toh Jing Hua; Zhoujun Cheng; Dongchan Shin; Fangyu Lei; Yitao Liu; Yiheng Xu; Shuyan Zhou; Silvio Savarese; Caiming Xiong; Victor Zhong; Tao Yu","publication_date":"2024","publication_year":"2024","publication_venue":"Advances in Neural Information Processing Systems 37: Datasets and Benchmarks Track (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"10.52202/079017-1650","publication_note":"Published in Advances in Neural Information Processing Systems 37: Datasets and Benchmarks Track (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"NeurIPS proceedings and DOI records","github_repo":"","github_stars":"","arxiv_id":"2404.07972","date_added":""},{"row_id":"ale-0430","title":"ToolBench","url":"https://arxiv.org/abs/2307.16789","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/28e50ee5b72e90b50e7196fde8ea260e-Abstract-Conference.html","annotation":"Tool-use benchmark and dataset for tool-augmented agents.","key_contribution":"Tool-use benchmark and dataset for tool-augmented agents.","novelty":"Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Tool-use benchmark and dataset for tool-augmented agents.","impact":"Use ToolBench to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2307.16789; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Yujia Qin; Shihao Liang; Yining Ye; Kunlun Zhu; Lan Yan; Yaxi Lu; Yankai Lin; Xin Cong; Xiangru Tang; Bill Qian; Sihan Zhao; Lauren Hong; Runchu Tian; Ruobing Xie; Jie Zhou; Mark Gerstein; Dahai Li; Zhiyuan Liu; Maosong Sun","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2307.16789","date_added":""},{"row_id":"ale-0431","title":"GAIA","url":"https://arxiv.org/abs/2311.12983","canonical_url":"https://arxiv.org/abs/2311.12983","annotation":"Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","key_contribution":"Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","impact":"Use GAIA to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2311.12983; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Grégoire Mialon; Clémentine Fourrier; Craig Swift; Thomas Wolf; Yann LeCun; Thomas Scialom","publication_date":"2023-11-21","publication_year":"2023","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2311.12983","date_added":""},{"row_id":"ale-0432","title":"Tau-bench","url":"https://arxiv.org/abs/2406.12045","canonical_url":"https://arxiv.org/abs/2406.12045","annotation":"Benchmark for tool-agent-user interactions in realistic domains.","key_contribution":"Benchmark for tool-agent-user interactions in realistic domains.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for tool-agent-user interactions in realistic domains.","impact":"Use Tau-bench to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2406.12045; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Shunyu Yao; Noah Shinn; Pedram Razavi; Karthik Narasimhan","publication_date":"2024-06-17","publication_year":"2024","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2406.12045","date_added":""},{"row_id":"ale-0433","title":"VisualWebArena","url":"https://arxiv.org/abs/2401.13649","canonical_url":"https://aclanthology.org/2024.acl-long.50/","annotation":"Visually grounded web-agent benchmark extending WebArena.","key_contribution":"Visually grounded web-agent benchmark extending WebArena.","novelty":"The work turns loop quality into a measurable task or score. Visually grounded web-agent benchmark extending WebArena.","impact":"Use VisualWebArena to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2401.13649; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Jing Yu Koh; Robert Lo; Lawrence Jang; Vikram Duvvur; Ming Chong Lim; Po-Yu Huang; Graham Neubig; Shuyan Zhou; Ruslan Salakhutdinov; Daniel Fried","publication_date":"2024","publication_year":"2024","publication_venue":"Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL)","publisher":"Association for Computational Linguistics","doi":"10.18653/v1/2024.acl-long.50","publication_note":"Published in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL); the linked arXiv record remains available for open access.","primary_category":"cs.LG","metadata_source":"ACL Anthology and DOI records","github_repo":"","github_stars":"","arxiv_id":"2401.13649","date_added":""},{"row_id":"ale-0434","title":"AppWorld","url":"https://arxiv.org/abs/2407.18901","canonical_url":"https://aclanthology.org/2024.acl-long.850/","annotation":"Benchmark of interactive app tasks with state-based and execution-based evaluation.","key_contribution":"Benchmark of interactive app tasks with state-based and execution-based evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Benchmark of interactive app tasks with state-based and execution-based evaluation.","impact":"Use AppWorld to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2407.18901; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;state","audience":"researcher;evaluator","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Harsh Trivedi; Tushar Khot; Mareike Hartmann; Ruskin Manku; Vinty Dong; Edward Li; Shashank Gupta; Ashish Sabharwal; Niranjan Balasubramanian","publication_date":"2024","publication_year":"2024","publication_venue":"Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL)","publisher":"Association for Computational Linguistics","doi":"10.18653/v1/2024.acl-long.850","publication_note":"Published in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ACL Anthology and DOI records","github_repo":"","github_stars":"","arxiv_id":"2407.18901","date_added":""},{"row_id":"ale-0435","title":"Vending-Bench","url":"https://arxiv.org/abs/2502.15840","canonical_url":"https://arxiv.org/abs/2502.15840","annotation":"Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.","key_contribution":"Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.","impact":"Use Vending-Bench to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2502.15840; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Axel Backlund; Lukas Petersson","publication_date":"2025-02-20","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2502.15840","date_added":""},{"row_id":"ale-0436","title":"Vending-Bench leaderboard","url":"https://andonlabs.com/evals/vending-bench","canonical_url":"https://andonlabs.com/evals/vending-bench","annotation":"Live long-horizon coherence results from Andon Labs.","key_contribution":"Live long-horizon coherence results from Andon Labs.","novelty":"The work turns loop quality into a measurable task or score. Live long-horizon coherence results from Andon Labs.","impact":"Use Vending-Bench leaderboard to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"andonlabs.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0437","title":"SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios","url":"https://arxiv.org/abs/2512.18470","canonical_url":"https://arxiv.org/abs/2512.18470","annotation":"Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","key_contribution":"Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","novelty":"The work turns loop quality into a measurable task or score. Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","impact":"Use SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Tue Le; Minh V. T. Thai; Dung Nguyen Manh; Huy Phan Nhat; Nghi D. Q. Bui","publication_date":"2025-12-20","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2512.18470","date_added":""},{"row_id":"ale-0438","title":"EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification","url":"https://arxiv.org/abs/2604.01687","canonical_url":"https://arxiv.org/abs/2604.01687","annotation":"A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","key_contribution":"A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","novelty":"Verification is promoted from a final check to a loop-control signal. A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","impact":"Use EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2604.01687; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Hanrong Zhang; Shicheng Fan; Henry Peng Zou; Yankai Chen; Zhenting Wang; Jiayu Zhou; Chengze Li; Wei-Chieh Huang; Yifei Yao; Kening Zheng; Xue Liu; Xiaoxiao Li; Philip S. Yu","publication_date":"2026-04-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code will be released","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.01687","date_added":""},{"row_id":"ale-0439","title":"SaaSBench: Coding Agents in Long-Horizon Enterprise SaaS Engineering","url":"https://arxiv.org/abs/2605.17526","canonical_url":"https://arxiv.org/abs/2605.17526","annotation":"Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","key_contribution":"Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","impact":"Use SaaSBench: Coding Agents in Long-Horizon Enterprise SaaS Engineering to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Qingnan Ren; Shun Zou; Shiting Huang; Ziao Zhang; Kou Shi; Zhen Fang; Yiming Zhao; Yu Zeng; Qisheng Su; Lin Chen; Yong Wang; Zehui Chen; Xiangxiang Chu; Feng Zhao","publication_date":"2026-05-17","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.17526","date_added":""},{"row_id":"ale-0440","title":"RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades","url":"https://arxiv.org/abs/2605.15846","canonical_url":"https://arxiv.org/abs/2605.15846","annotation":"115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","key_contribution":"115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","novelty":"The work targets tasks that exceed a single context window or prompt session. 115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","impact":"Use RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xinbo Xu; Ruihan Yang; Haiyang Shen; Wendong Xu; Bofei Gao; Ruoyu Wu; Kean Shi; Weichu Xie; Xuanzhong Chen; Ming Wu; Jason Zeng; Michael Heinrich; Elvis Zhang; Liang Chen; Kuan Li; Baobao Chang","publication_date":"2026-05-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"30 pages, 15 figures","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.15846","date_added":""},{"row_id":"ale-0441","title":"RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code","url":"https://arxiv.org/abs/2503.07832","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2025/hash/6b44ee74539ea77d6a0d50d468724371-Abstract-Conference.html","annotation":"Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","key_contribution":"Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","impact":"Use RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"state","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Dhruv Gautam; Spandan Garg; Jinu Jang; Neel Sundaresan; Roshanak Zilouchian Moghaddam","publication_date":"2025","publication_year":"2025","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2503.07832","date_added":""},{"row_id":"ale-0442","title":"RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents","url":"https://arxiv.org/abs/2606.22678","canonical_url":"https://arxiv.org/abs/2606.22678","annotation":"Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","key_contribution":"Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","novelty":"Verification is promoted from a final check to a loop-control signal. Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","impact":"Use RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Meher Bhaskar Madiraju; Meher Sai Preetam Madiraju","publication_date":"2026-06-21","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"9 pages, 7 tables, 1 figure","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.22678","date_added":""},{"row_id":"ale-0443","title":"SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks","url":"https://arxiv.org/abs/2603.24755","canonical_url":"https://arxiv.org/abs/2603.24755","annotation":"Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","key_contribution":"Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","impact":"Use SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;state;budget","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Gabriel Orlanski; Devjeet Roy; Alexander Yun; Changho Shin; Alex Gu; Albert Ge; Dyah Adila; Nicholas Roberts; Frederic Sala; Aws Albarghouthi","publication_date":"2026-03-25","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"10.5281/zenodo.18405900,","publication_note":"Code and Leaderboards are located at https://www.scbench.ai","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.24755","date_added":""},{"row_id":"ale-0444","title":"LongCLI-Bench: A Preliminary Benchmark for Long-horizon Agentic Programming in Command-Line Interfaces","url":"https://arxiv.org/abs/2602.14337","canonical_url":"https://aclanthology.org/2026.findings-acl.1497/","annotation":"Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","key_contribution":"Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","novelty":"The work turns loop quality into a measurable task or score. Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","impact":"Use LongCLI-Bench: A Preliminary Benchmark for Long-horizon Agentic Programming in Command-Line Interfaces to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Yukang Feng; Jianwen Sun; Zelai Yang; Jiaxin Ai; Chuanhao Li; Zizhen Li; Fanrui Zhang; Kang He; Rui Ma; Jifan Lin; Jie Sun; Yang Xiao; Sizhuo Zhou; Wenxiao Wu; Yiming Liu; Pengfei Liu; Yu Qiao; Shenglin Zhang; Kaipeng Zhang","publication_date":"2026","publication_year":"2026","publication_venue":"Findings of the Association for Computational Linguistics: ACL","publisher":"Association for Computational Linguistics","doi":"10.18653/v1/2026.findings-acl.1497","publication_note":"Published in Findings of the Association for Computational Linguistics: ACL; the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ACL Anthology and DOI records","github_repo":"","github_stars":"","arxiv_id":"2602.14337","date_added":""},{"row_id":"ale-0445","title":"Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?","url":"https://arxiv.org/abs/2606.29920","canonical_url":"https://arxiv.org/abs/2606.29920","annotation":"Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","key_contribution":"Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","impact":"Use Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios? to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Yangda Peng; Yunjia Qi; Hao Peng; Haotian Xia; Guanzhong He; Xintong Shi; Richeng Xuan; Songyuanyi Lu; Yixian Liu; Zhichao Hu; Yuhong Liu; Lei Hou; Bin Xu; Juanzi Li","publication_date":"2026-06-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.29920","date_added":""},{"row_id":"ale-0446","title":"SentinelBench: A Benchmark for Long-Running Monitoring Agents","url":"https://arxiv.org/abs/2606.05342","canonical_url":"https://arxiv.org/abs/2606.05342","annotation":"Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","key_contribution":"Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","novelty":"The work turns loop quality into a measurable task or score. Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","impact":"Use SentinelBench: A Benchmark for Long-Running Monitoring Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Matheus Kunzler Maldaner; Adam Fourney; Amanda Swearngin; Hussein Mozannar; Gagan Bansal; Maya Murad; Rafah Hosn; Saleema Amershi","publication_date":"2026-06-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"18 pages, 16 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.05342","date_added":""},{"row_id":"ale-0447","title":"SWE-Together: Evaluating Coding Agents in Interactive User Sessions","url":"https://arxiv.org/abs/2606.29957","canonical_url":"https://arxiv.org/abs/2606.29957","annotation":"Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","key_contribution":"Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","novelty":"The work turns loop quality into a measurable task or score. Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","impact":"Use SWE-Together: Evaluating Coding Agents in Interactive User Sessions to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Yifan Wu; Zhuokai Zhao; Songlin Li; Ho Hin Lee; Jiacheng Zhu; Shirley Wu; Tianhe Yu; Serena Li; Lizhu Zhang; Xiangjun Fan; Shengzhi Li","publication_date":"2026-06-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.29957","date_added":""},{"row_id":"ale-0448","title":"The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break","url":"https://arxiv.org/abs/2604.11978","canonical_url":"https://arxiv.org/abs/2604.11978","annotation":"Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","key_contribution":"Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","novelty":"The work turns loop quality into a measurable task or score. Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","impact":"Use The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xinyu Jessica Wang; Haoyue Bai; Yiyou Sun; Haorui Wang; Shuibai Zhang; Wenjie Hu; Mya Schroder; Bilge Mutlu; Dawn Song; Robert D Nowak","publication_date":"2026-04-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.11978","date_added":""},{"row_id":"ale-0449","title":"Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents","url":"https://arxiv.org/abs/2603.29231","canonical_url":"https://arxiv.org/abs/2603.29231","annotation":"Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","key_contribution":"Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","novelty":"The work targets tasks that exceed a single context window or prompt session. Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","impact":"Use Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2603.29231; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Aaditya Khanal; Yangyang Tao; Junxiu Zhou","publication_date":"2026-03-31","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"23 pages, 4 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.29231","date_added":""},{"row_id":"ale-0450","title":"SEAGym: An Evaluation Environment for Self-Evolving LLM Agents","url":"https://arxiv.org/abs/2606.17546","canonical_url":"https://arxiv.org/abs/2606.17546","annotation":"Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","key_contribution":"Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","impact":"Use SEAGym: An Evaluation Environment for Self-Evolving LLM Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;context;verification;budget","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Congjie Zheng; Chuanyi Xue; Bin Liang; Jun Yang; Changshui Zhang","publication_date":"2026-06-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.17546","date_added":""},{"row_id":"ale-0451","title":"EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions","url":"https://arxiv.org/abs/2605.24110","canonical_url":"https://arxiv.org/abs/2605.24110","annotation":"Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","key_contribution":"Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","impact":"Use EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Haiyang Shen; Xuanzhong Chen; Wendong Xu; Yun Ma; Liang Chen; Kuan Li","publication_date":"2026-05-22","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Work in Progress; 32 pages, 10 figures, preprint","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.24110","date_added":""},{"row_id":"ale-0452","title":"On the Reliability of Computer Use Agents","url":"https://arxiv.org/abs/2604.17849","canonical_url":"https://arxiv.org/abs/2604.17849","annotation":"Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","key_contribution":"Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","impact":"Use On the Reliability of Computer Use Agents to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2604.17849; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Gonzalo Gonzalez-Pumariega; Saaket Agashe; Jiachen Yang; Ang Li; Xin Eric Wang","publication_date":"2026-04-20","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"33 pages, 3 figures, 4 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.17849","date_added":""},{"row_id":"ale-0453","title":"AgentLens: Revealing the Lucky Pass Problem in SWE-Agent Evaluation","url":"https://arxiv.org/abs/2605.12925","canonical_url":"https://arxiv.org/abs/2605.12925","annotation":"Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","key_contribution":"Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","impact":"Use AgentLens: Revealing the Lucky Pass Problem in SWE-Agent Evaluation to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2605.12925; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Priyam Sahoo; Gaurav Mittal; Xiaomin Li; Shengjie Ma; Benjamin Steenhoek; Pingping Lin; Yu Hu","publication_date":"2026-05-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.12925","date_added":""},{"row_id":"ale-0454","title":"ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction","url":"https://arxiv.org/abs/2601.21008","canonical_url":"https://openreview.net/pdf/16a0193aa4e71ffe6c921ac0081a66b525eea017.pdf","annotation":"Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","key_contribution":"Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","novelty":"Verification is promoted from a final check to a loop-control signal. Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","impact":"Use ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"trigger;verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Ruicheng Ao; David Simchi-Levi; Xinshang Wang","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 43rd International Conference on Machine Learning (ICML), PMLR 306","publisher":"PMLR","doi":"","publication_note":"Published in Proceedings of the 43rd International Conference on Machine Learning (ICML), PMLR 306; the linked arXiv record remains available for open access.","primary_category":"cs.LG","metadata_source":"PMLR camera-ready record","github_repo":"","github_stars":"","arxiv_id":"2601.21008","date_added":""},{"row_id":"ale-0455","title":"LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis","url":"https://arxiv.org/abs/2605.30434","canonical_url":"https://arxiv.org/abs/2605.30434","annotation":"Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","key_contribution":"Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","novelty":"The work turns loop quality into a measurable task or score. Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","impact":"Use LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;state","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Kewei Xu; Xiaoben Lu; Shuofei Qiao; Zihan Ding; Haoming Xu; Lei Liang; Ningyu Zhang","publication_date":"2026-05-28","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Ongoing work","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.30434","date_added":""},{"row_id":"ale-0456","title":"MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks","url":"https://arxiv.org/abs/2602.16313","canonical_url":"https://arxiv.org/abs/2602.16313","annotation":"Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","key_contribution":"Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","novelty":"The work turns loop quality into a measurable task or score. Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","impact":"Use MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Zexue He; Yu Wang; Churan Zhi; Yuanzhe Hu; Tzu-Ping Chen; Lang Yin; Ze Chen; Tong Arthur Wu; Siru Ouyang; Zihan Wang; Jiaxin Pei; Julian McAuley; Yejin Choi; Alex Pentland","publication_date":"2026-02-18","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2602.16313","date_added":""},{"row_id":"ale-0457","title":"Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations","url":"https://arxiv.org/abs/2606.00832","canonical_url":"https://arxiv.org/abs/2606.00832","annotation":"Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","key_contribution":"Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","impact":"Use Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;context;verification;state;exit","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Adril Putra Merin; David Anugraha; Ayu Purwarianti; Genta Indra Winata","publication_date":"2026-05-30","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Preprint","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.00832","date_added":""},{"row_id":"ale-0458","title":"π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows","url":"https://arxiv.org/abs/2605.14678","canonical_url":"https://arxiv.org/abs/2605.14678","annotation":"Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","key_contribution":"Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","novelty":"The work turns loop quality into a measurable task or score. Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","impact":"Use π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Haoran Zhang; Luxin Xu; Zhilin Wang; Runquan Gui; Shunkai Zhang; Haodi Lei; Zihao He; Bingsu He; Chicheng Qin; Tong Zhu; Xiaoye Qu; Yang Yang; Yu Cheng; Yafu Li","publication_date":"2026-05-14","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"44 pages","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.14678","date_added":""},{"row_id":"ale-0459","title":"Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation","url":"https://arxiv.org/abs/2603.23638","canonical_url":"https://arxiv.org/abs/2603.23638","annotation":"A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","key_contribution":"A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","novelty":"The work targets tasks that exceed a single context window or prompt session. A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","impact":"Use Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"state","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Yi Han; Yan Wang; Lingfei Qian; Haohang Li; Yupeng Cao; Yueru He; Xueqing Peng; Nanhan Shen; Yitao Xu; Yankai Chen; Dongji Feng; Jimin Huang; Xue Liu; Jian-Yun Nie; Sophia Ananiadou","publication_date":"2026-03-24","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.23638","date_added":""},{"row_id":"ale-0460","title":"EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer","url":"https://arxiv.org/abs/2607.05202","canonical_url":"https://arxiv.org/abs/2607.05202","annotation":"Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","key_contribution":"Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","impact":"Use EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xingze Gao; Chuanrui Hu; Hongda Chen; Pengfei Yao; Zhao Wang; Yi Bai; Zhengwei Wu; Yunyun Han; Xiaofeng Cong; Jie Gui; Yafeng Deng; Teng Li","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"15 pages, 2 figures, 8 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05202","date_added":""},{"row_id":"ale-0461","title":"AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents","url":"https://arxiv.org/abs/2607.02255","canonical_url":"https://arxiv.org/abs/2607.02255","annotation":"Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","key_contribution":"Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","novelty":"Persistent memory is treated as an external runtime artifact. Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","impact":"Use AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xiangchen Cheng; Yunwei Jiang; Jianwen Sun; Zizhen Li; Chuanhao Li; Xiangcheng Cao; Yihao Liu; Fanrui Zhang; Li Jin; Kaipeng Zhang","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02255","date_added":""},{"row_id":"ale-0462","title":"Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops","url":"https://arxiv.org/abs/2607.05197","canonical_url":"https://arxiv.org/abs/2607.05197","annotation":"Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","key_contribution":"Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","impact":"Use Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.05197; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"delegation;verification;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Tobias Kiecker; Eik Reichmann; Hosung Kang; Gabin An; Lars Grunske","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"4 Pages (+1 for references), NIER Paper","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05197","date_added":""},{"row_id":"ale-0463","title":"DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks","url":"https://arxiv.org/abs/2607.07946","canonical_url":"https://arxiv.org/abs/2607.07946","annotation":"113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","key_contribution":"113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","novelty":"The work targets tasks that exceed a single context window or prompt session. 113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","impact":"Use DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Wenqi Huang; Charley Lee; Leonard Tng; Serena Ge","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"32 pages, 10 figures. Code and data: https://github.com/datacurve-ai/deep-swe ; https://deepswe.datacurve.ai/","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07946","date_added":""},{"row_id":"ale-0464","title":"PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization","url":"https://arxiv.org/abs/2607.07744","canonical_url":"https://arxiv.org/abs/2607.07744","annotation":"Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","key_contribution":"Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","novelty":"Verification is promoted from a final check to a loop-control signal. Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","impact":"Use PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Yingyun Cui; Yi Xie; Piaohong Wang; Jiawei Ma; Bo Liu; Liangliang Cao","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07744","date_added":""},{"row_id":"ale-0465","title":"Benchmarking coding agents on Databricks' multi-million line codebase","url":"https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase","canonical_url":"https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase","annotation":"Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","key_contribution":"Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","novelty":"Verification is promoted from a final check to a loop-control signal. Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","impact":"Use Benchmarking coding agents on Databricks' multi-million line codebase to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;verification;budget","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"","publication_date":"2026","publication_year":"2026","publication_venue":"","publisher":"Databricks","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0466","title":"UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks","url":"https://arxiv.org/abs/2607.08768","canonical_url":"https://arxiv.org/abs/2607.08768","annotation":"Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","key_contribution":"Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","impact":"Use UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;context;verification;state;escalation","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Zhekai Chen; Chengqi Duan; Kaiyue Sun; Bohao Li; Yuqing Wang; Manyuan Zhang; Xihui Liu","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Project Page: https://uniclawbench.github.io | GitHub Repo: https://github.com/HKU-MMLab/UniClawBench","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08768","date_added":""},{"row_id":"ale-0467","title":"SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills","url":"https://arxiv.org/abs/2607.09016","canonical_url":"https://arxiv.org/abs/2607.09016","annotation":"Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","key_contribution":"Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","impact":"Use SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xuan Chen; Chengpeng Wang; Lu Yan; Xiangyu Zhang","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09016","date_added":""},{"row_id":"ale-0468","title":"SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution","url":"https://arxiv.org/abs/2603.13428","canonical_url":"https://arxiv.org/abs/2603.13428","annotation":"Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","key_contribution":"Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","impact":"Use SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Gangda Deng; Zhaoling Chen; Zhongming Yu; Haoyang Fan; Yuhong Liu; Yuxin Yang; Dhruv Parikh; Rajgopal Kannan; Le Cong; Mengdi Wang; Qian Zhang; Viktor Prasanna; Xiangru Tang; Xingyao Wang","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 43rd International Conference on Machine Learning (ICML)","publisher":"PMLR","doi":"","publication_note":"Accepted at Proceedings of the 43rd International Conference on Machine Learning (ICML); the linked arXiv record is the available paper version.","primary_category":"cs.SE","metadata_source":"Current arXiv acceptance note and official project record","github_repo":"","github_stars":"","arxiv_id":"2603.13428","date_added":""},{"row_id":"ale-0469","title":"AgentAbstain: Do LLM Agents Know When Not to Act?","url":"https://arxiv.org/abs/2607.10059","canonical_url":"https://arxiv.org/abs/2607.10059","annotation":"Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","key_contribution":"Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","novelty":"The work turns loop quality into a measurable task or score. Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","impact":"Use AgentAbstain: Do LLM Agents Know When Not to Act? to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xun Liu; Yi Evie Zhang; Vira Kasprova; Parisa Rabbani; Pardis Sadat Zahraei; Tianyu Zhang; Ali Ebrahimpour-Boroojeny; Varun Chandrasekaran","publication_date":"2026-07-11","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"56 pages, 13 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.10059","date_added":"2026-07-15"},{"row_id":"ale-0470","title":"Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy","url":"https://arxiv.org/abs/2607.10526","canonical_url":"https://arxiv.org/abs/2607.10526","annotation":"Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","key_contribution":"Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","novelty":"The work turns loop quality into a measurable task or score. Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","impact":"Use Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;verification;state","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xutao Mao; Liangjie Zhao; Leyao Wang; Rui Qian; Qiang Huang; Wentao Wang; Bo Han; Xiang Zheng; Cong Wang","publication_date":"2026-07-12","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.10526","date_added":"2026-07-15"},{"row_id":"ale-0471","title":"Set-shifting Behavioral Test for Harnessed Agents","url":"https://arxiv.org/abs/2607.13396","canonical_url":"https://arxiv.org/abs/2607.13396","annotation":"Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","key_contribution":"Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","impact":"Use Set-shifting Behavioral Test for Harnessed Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Ziwei Ye","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13396","date_added":"2026-07-17"},{"row_id":"ale-0472","title":"MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers","url":"https://arxiv.org/abs/2607.14642","canonical_url":"https://arxiv.org/abs/2607.14642","annotation":"Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","key_contribution":"Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","impact":"Use MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Huanxi Liu; Kun Hu; Jiaqi Liao; Qiang Wang; Pengfei Qian; YuanZhao Zhai; Dawei Feng; Bo Ding; Huaimin Wang","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14642","date_added":"2026-07-17"},{"row_id":"ale-0473","title":"MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization","url":"https://arxiv.org/abs/2607.15205","canonical_url":"https://arxiv.org/abs/2607.15205","annotation":"Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","key_contribution":"Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","novelty":"The work turns loop quality into a measurable task or score. Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","impact":"Use MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Shaoxiong Zhan; Shi Hu; Boyu Feng; Hai Lin; Andrew Gong; Zhengda Zhou; Jiaying Zhou; Yunyun Hou; Hao Su; Hai-Tao Zheng","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.15205","date_added":"2026-07-17"},{"row_id":"ale-0474","title":"Agentic Engineering: The Agent Loop","url":"https://junpingyi.com/books/agentic-engineering/agent-loop/","canonical_url":"https://junpingyi.com/books/agentic-engineering/agent-loop/","annotation":"Minimal mental model for the loop underlying agent operation.","key_contribution":"Minimal mental model for the loop underlying agent operation.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Minimal mental model for the loop underlying agent operation.","impact":"Use Agentic Engineering: The Agent Loop to bound risk before recurring or unattended execution.","signal":"Contextual source from junpingyi.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake;budget;escalation;exit","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"junpingyi.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0475","title":"The agent loop: ReAct, plan-and-execute, reflection","url":"https://www.kunwar.page/chapter/067-the-agent-loop-react-plan-and-execute-reflection","canonical_url":"https://www.kunwar.page/chapter/067-the-agent-loop-react-plan-and-execute-reflection","annotation":"Practical walkthrough of the base loop and common variants.","key_contribution":"Practical walkthrough of the base loop and common variants.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Practical walkthrough of the base loop and common variants.","impact":"Use The agent loop: ReAct, plan-and-execute, reflection to bound risk before recurring or unattended execution.","signal":"Contextual source from www.kunwar.page; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake;budget;escalation;exit","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"kunwar.page","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0476","title":"How to Build an Agent","url":"https://ampcode.com/how-to-build-an-agent","canonical_url":"https://ampcode.com/notes/how-to-build-an-agent","annotation":"Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","key_contribution":"Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","impact":"Use How to Build an Agent to bound risk before recurring or unattended execution.","signal":"Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"budget","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"ampcode.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0477","title":"Agentic Coding Recommendations","url":"https://lucumr.pocoo.org/2025/6/12/agentic-coding/","canonical_url":"https://lucumr.pocoo.org/2025/6/12/agentic-coding/","annotation":"Armin Ronacher's field notes on which practices hold up when agents do most of the work.","key_contribution":"Armin Ronacher's field notes on which practices hold up when agents do most of the work.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Armin Ronacher's field notes on which practices hold up when agents do most of the work.","impact":"Use Agentic Coding Recommendations to bound risk before recurring or unattended execution.","signal":"Contextual source from lucumr.pocoo.org; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake;budget;escalation;exit","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2025-06-12","publication_year":"2025","publication_venue":"","publisher":"Armin Ronacher's Thoughts and Writings","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0478","title":"Coding Agents 101: The Art of Actually Getting Things Done","url":"https://devin.ai/agents101","canonical_url":"https://devin.ai/agents101","annotation":"Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.","key_contribution":"Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.","impact":"Use Coding Agents 101: The Art of Actually Getting Things Done to bound risk before recurring or unattended execution.","signal":"Contextual source from devin.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"delegation;exit","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"devin.ai","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0479","title":"How Anthropic teams use Claude Code","url":"https://claude.com/blog/how-anthropic-teams-use-claude-code","canonical_url":"https://claude.com/blog/how-anthropic-teams-use-claude-code","annotation":"Cross-team field report of real recurring agent workflows in engineering, security, and data science.","key_contribution":"Cross-team field report of real recurring agent workflows in engineering, security, and data science.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Cross-team field report of real recurring agent workflows in engineering, security, and data science.","impact":"Use How Anthropic teams use Claude Code to bound risk before recurring or unattended execution.","signal":"Contextual source from claude.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake;budget;escalation;exit","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0480","title":"How Boris Uses Claude Code","url":"https://howborisusesclaudecode.com/","canonical_url":"https://howborisusesclaudecode.com/","annotation":"Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","key_contribution":"Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","impact":"Use How Boris Uses Claude Code to bound risk before recurring or unattended execution.","signal":"Contextual source from howborisusesclaudecode.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"objective;trigger;workspace;exit","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"@CarolinaCherry","publication_date":"","publication_year":"","publication_venue":"","publisher":"How Boris Uses Claude Code","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0481","title":"Agent of the Day: Copilot Agent PR Analysis","url":"https://github.github.com/gh-aw/blog/2026-05-26-agent-of-the-day/","canonical_url":"https://github.github.com/gh-aw/blog/2026-05-26-agent-of-the-day/","annotation":"Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","key_contribution":"Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","novelty":"Primary-source operational guidance rather than commentary. Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","impact":"Use Agent of the Day: Copilot Agent PR Analysis to bound risk before recurring or unattended execution.","signal":"Contextual source from github.github.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"GitHub Agentic Workflows","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0482","title":"Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows","url":"https://arxiv.org/abs/2607.07052","canonical_url":"https://arxiv.org/abs/2607.07052","annotation":"Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","key_contribution":"Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. 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Repeats a failing deterministic check with durable progress, duplicate-failure detection, and a hard retry budget.","impact":"Use Runnable test-repair loop to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Template","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Examples And Schema","section_slug":"examples-and-schema","lifecycle_stages":"verification;budget","audience":"builder;operator","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0498","title":"Runnable loop guide","url":"examples/runnable/README.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/runnable/README.md","annotation":"Compares 8 starters by trigger, state, gate, and runtime, including executable test-repair, threshold-monitor, and queue-worker loops.","key_contribution":"Compares 8 starters by trigger, state, gate, and runtime, including executable test-repair, threshold-monitor, and queue-worker loops.","novelty":"State persistence is explicit enough for repeated runs and handoff. Compares 8 starters by trigger, state, gate, and runtime, including executable test-repair, threshold-monitor, and queue-worker loops.","impact":"Use Runnable loop guide to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Template","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Examples And Schema","section_slug":"examples-and-schema","lifecycle_stages":"trigger;intake;verification;state","audience":"builder;operator","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0499","title":"Loop gallery guide","url":"gallery/README.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/README.md","annotation":"Quality bar for contributed loop examples with receipts and lessons learned.","key_contribution":"Quality bar for contributed loop examples with receipts and lessons learned.","novelty":"The resource is directly reusable as a starting artifact. Quality bar for contributed loop examples with receipts and lessons learned.","impact":"Use Loop gallery guide to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Template","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Community Gallery","section_slug":"community-gallery","lifecycle_stages":"state","audience":"builder;operator","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0500","title":"Loop gallery template","url":"gallery/template.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/template.md","annotation":"Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.","key_contribution":"Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.","novelty":"Verification is promoted from a final check to a loop-control signal. Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.","impact":"Use Loop gallery template to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Template","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Community Gallery","section_slug":"community-gallery","lifecycle_stages":"trigger;intake;verification;state;escalation","audience":"builder;operator","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0501","title":"PR babysitter reference loop","url":"gallery/pr-babysitter-reference.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/pr-babysitter-reference.md","annotation":"Reference gallery entry for keeping a pull request moving.","key_contribution":"Reference gallery entry for keeping a pull request moving.","novelty":"Turns loop adoption into shareable cases with enough structure to compare lessons learned. Reference gallery entry for keeping a pull request moving.","impact":"Use PR babysitter reference loop to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Community Gallery","section_slug":"community-gallery","lifecycle_stages":"whole-loop","audience":"builder;operator","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0502","title":"CI repair reference loop","url":"gallery/ci-repair-reference.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/ci-repair-reference.md","annotation":"Reference gallery entry for turning failing CI into a verified patch or escalation.","key_contribution":"Reference gallery entry for turning failing CI into a verified patch or escalation.","novelty":"Verification is promoted from a final check to a loop-control signal. Reference gallery entry for turning failing CI into a verified patch or escalation.","impact":"Use CI repair reference loop to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Community Gallery","section_slug":"community-gallery","lifecycle_stages":"verification;escalation","audience":"builder;operator","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0503","title":"Docs drift reference loop","url":"gallery/docs-drift-reference.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/docs-drift-reference.md","annotation":"Reference gallery entry for recurring docs/code consistency checks.","key_contribution":"Reference gallery entry for recurring docs/code consistency checks.","novelty":"Turns loop adoption into shareable cases with enough structure to compare lessons learned. Reference gallery entry for recurring docs/code consistency checks.","impact":"Use Docs drift reference loop to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Community Gallery","section_slug":"community-gallery","lifecycle_stages":"whole-loop","audience":"builder;operator","evidence_class":"repository-native","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0504","title":"Most Developers Do Not Need Agent Loops Yet","url":"https://alphasignalai.substack.com/p/most-developers-do-not-need-agent","canonical_url":"https://alphasignalai.substack.com/p/most-developers-do-not-need-agent","annotation":"Useful caution against adopting loops before the task, signal, and economics justify them.","key_contribution":"Useful caution against adopting loops before the task, signal, and economics justify them.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Useful caution against adopting loops before the task, signal, and economics justify them.","impact":"Use Most Developers Do Not Need Agent Loops Yet to bound risk before recurring or unattended execution.","signal":"Contextual source from alphasignalai.substack.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget;escalation;exit","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"AlphaSignal AI","publication_date":"","publication_year":"","publication_venue":"","publisher":"Substack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0505","title":"Engineering Agentic Systems for Reliability","url":"https://pruningmypothos.com/systems/engineering-agentic-systems-for-reliability/","canonical_url":"https://pruningmypothos.com/systems/engineering-agentic-systems-for-reliability/","annotation":"Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.","key_contribution":"Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.","novelty":"Verification is promoted from a final check to a loop-control signal. Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.","impact":"Use Engineering Agentic Systems for Reliability to bound risk before recurring or unattended execution.","signal":"Contextual source from pruningmypothos.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"workspace;verification;escalation","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"Shailesh Rawat","publication_date":"","publication_year":"","publication_venue":"","publisher":"Sans Serif Systems","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0506","title":"Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared","url":"https://callsphere.ai/blog/self-correcting-agents-reflexion-critic-react-loops-compared-2026","canonical_url":"https://callsphere.ai/blog/self-correcting-agents-reflexion-critic-react-loops-compared-2026","annotation":"Compares self-correction patterns and their cost/failure tradeoffs.","key_contribution":"Compares self-correction patterns and their cost/failure tradeoffs.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Compares self-correction patterns and their cost/failure tradeoffs.","impact":"Use Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared to bound risk before recurring or unattended execution.","signal":"Contextual source from callsphere.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification;budget","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"CallSphere","publication_date":"2026-04-24","publication_year":"2026","publication_venue":"","publisher":"CallSphere","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0507","title":"How to Build an AI Agent Harness: A 2026 Complete Guide","url":"https://atlan.com/know/how-to-build-ai-agent-harness/","canonical_url":"https://atlan.com/know/how-to-build-ai-agent-harness/","annotation":"Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","key_contribution":"Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","impact":"Use How to Build an AI Agent Harness: A 2026 Complete Guide to bound risk before recurring or unattended execution.","signal":"Contextual source from atlan.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"workspace;context;verification","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"atlan.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0508","title":"Harness Engineering vs Prompt Engineering vs Context Engineering Explained","url":"https://medium.com/@visrow/harness-engineering-vs-prompt-engineering-vs-context-engineering-explained-0423b692c87d","canonical_url":"https://medium.com/@visrow/harness-engineering-vs-prompt-engineering-vs-context-engineering-explained-0423b692c87d","annotation":"Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.","key_contribution":"Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.","impact":"Use Harness Engineering vs Prompt Engineering vs Context Engineering Explained to bound risk before recurring or unattended execution.","signal":"Contextual source from medium.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"context","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"Vishal Mysore","publication_date":"2026-05-19","publication_year":"2026","publication_venue":"","publisher":"Medium","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0509","title":"Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering","url":"https://arxiv.org/abs/2606.17799","canonical_url":"https://arxiv.org/abs/2606.17799","annotation":"Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","key_contribution":"Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","novelty":"The work turns loop quality into a measurable task or score. Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","impact":"Use Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2606.17799; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Maria I. Gorinova; Macey Baker; Amy Heineike; Maksim Shaposhnikov; Rob Willoughby; Dru Knox","publication_date":"2026-06-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.17799","date_added":""},{"row_id":"ale-0510","title":"Understanding the Challenges in Iterative Generative Optimization with LLMs","url":"https://arxiv.org/abs/2603.23994","canonical_url":"https://arxiv.org/abs/2603.23994","annotation":"Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","key_contribution":"Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","impact":"Use Understanding the Challenges in Iterative Generative Optimization with LLMs to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2603.23994; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget;escalation;exit","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Allen Nie; Xavier Daull; Zhiyi Kuang; Abhinav Akkiraju; Anish Chaudhuri; Max Piasevoli; Ryan Rong; YuCheng Yuan; Prerit Choudhary; Shannon Xiao; Rasool Fakoor; Adith Swaminathan; Ching-An Cheng","publication_date":"2026-03-25","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"39 pages, 17 figures","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.23994","date_added":""},{"row_id":"ale-0511","title":"The Illusion of Multi-Agent Advantage","url":"https://arxiv.org/abs/2606.13003","canonical_url":"https://arxiv.org/abs/2606.13003","annotation":"Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","key_contribution":"Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","impact":"Use The Illusion of Multi-Agent Advantage to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2606.13003; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Prathyusha Jwalapuram; Hehai Lin; Chuyuan Li; Fangkai Jiao; Sudong Wang; Yifei Ming; Zixuan Ke; Chengwei Qin; Giuseppe Carenini; Shafiq Joty","publication_date":"2026-06-11","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.13003","date_added":""},{"row_id":"ale-0512","title":"The Coming Loop","url":"https://lucumr.pocoo.org/2026/6/23/the-coming-loop/","canonical_url":"https://lucumr.pocoo.org/2026/6/23/the-coming-loop/","annotation":"Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","key_contribution":"Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","impact":"Use The Coming Loop to bound risk before recurring or unattended execution.","signal":"Contextual source from lucumr.pocoo.org; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"escalation;exit","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-06-23","publication_year":"2026","publication_venue":"","publisher":"Armin Ronacher's Thoughts and Writings","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0513","title":"Loop Engineering, the Latest AI Buzzword, Still Needs Humans in the Loop","url":"https://www.theregister.com/ai-and-ml/2026/06/24/loop-engineering-latest-ai-buzzword-still-needs-humans-in-the-loop/5261735","canonical_url":"https://www.theregister.com/ai-and-ml/2026/06/24/loop-engineering-latest-ai-buzzword-still-needs-humans-in-the-loop/5261735","annotation":"The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","key_contribution":"The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","impact":"Use Loop Engineering, the Latest AI Buzzword, Still Needs Humans in the Loop to bound risk before recurring or unattended execution.","signal":"Contextual source from www.theregister.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"","publisher":"theregister","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0514","title":"When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents","url":"https://arxiv.org/abs/2607.01641","canonical_url":"https://arxiv.org/abs/2607.01641","annotation":"Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","key_contribution":"Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","impact":"Use When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.01641; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"workspace;delegation;exit","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xinyi Hou; Shenao Wang; Yanjie Zhao; Haoyu Wang","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.01641","date_added":""},{"row_id":"ale-0515","title":"The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents","url":"https://arxiv.org/abs/2607.07436","canonical_url":"https://arxiv.org/abs/2607.07436","annotation":"Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.","key_contribution":"Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.","impact":"Use The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07436; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget;escalation;exit","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xing Zhang; Yanwei Cui; Guanghui Wang; Ziyuan Li; Wei Qiu; Bing Zhu; Peiyang He","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07436","date_added":""},{"row_id":"ale-0516","title":"Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows","url":"https://arxiv.org/abs/2607.07504","canonical_url":"https://arxiv.org/abs/2607.07504","annotation":"Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","key_contribution":"Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","impact":"Use Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07504; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget;escalation;exit","audience":"researcher;evaluator;operator;security","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Wei-Jung Huang","publication_date":"2026","publication_year":"2026","publication_venue":"KDD Workshop on AI Data Scientist","publisher":"ACM SIGKDD","doi":"","publication_note":"Accepted at KDD Workshop on AI Data Scientist; the linked arXiv record is the available paper version.","primary_category":"cs.AI","metadata_source":"Current arXiv acceptance note and official workshop page","github_repo":"","github_stars":"","arxiv_id":"2607.07504","date_added":""},{"row_id":"ale-0517","title":"The Verification Horizon: No Silver Bullet for Coding Agent Rewards","url":"https://arxiv.org/abs/2606.26300","canonical_url":"https://arxiv.org/abs/2606.26300","annotation":"Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","key_contribution":"Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","novelty":"Verification is promoted from a final check to a loop-control signal. Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","impact":"Use The Verification Horizon: No Silver Bullet for Coding Agent Rewards to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2606.26300; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Binghai Wang; Chenlong Zhang; Dayiheng Liu; Jiajun Zhang; Jiawei Chen; Mingze Li; Mouxiang Chen; Rongyao Fang; Siyuan Zhang; Xuwu Wang; Yuheng Jing; Zeyao Ma; Zeyu Cui","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Authors are listed alphabetically by their first names","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.26300","date_added":""},{"row_id":"ale-0518","title":"Write Code Like a Human Will Maintain It","url":"https://unstack.io/write-code-like-a-human-will-maintain-it","canonical_url":"https://unstack.io/write-code-like-a-human-will-maintain-it","annotation":"Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","key_contribution":"Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","impact":"Use Write Code Like a Human Will Maintain It to bound risk before recurring or unattended execution.","signal":"Contextual source from unstack.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"escalation","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"","publisher":"Unstack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0519","title":"Claude Code Sends 33k Tokens Before Reading the Prompt","url":"https://systima.ai/blog/claude-code-vs-opencode-token-overhead","canonical_url":"https://systima.ai/blog/claude-code-vs-opencode-token-overhead","annotation":"July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","key_contribution":"July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","impact":"Use Claude Code Sends 33k Tokens Before Reading the Prompt to bound risk before recurring or unattended execution.","signal":"Contextual source from systima.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"delegation;budget","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Systima","publication_date":"2026-07-12","publication_year":"2026","publication_venue":"","publisher":"Systima","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0520","title":"Rethinking the Evaluation of Harness Evolution for Agents","url":"https://arxiv.org/abs/2607.12227","canonical_url":"https://arxiv.org/abs/2607.12227","annotation":"Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","key_contribution":"Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","impact":"Use Rethinking the Evaluation of Harness Evolution for Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.12227; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification;budget","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yike Wang; Huaisheng Zhu; Zhengyu Hu; Yige Yuan; Zhengyu Chen; Shakti Senthil; Hannaneh Hajishirzi; Yulia Tsvetkov; Pradeep Dasigi; Teng Xiao","publication_date":"2026-07-14","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.12227","date_added":"2026-07-17"},{"row_id":"ale-0521","title":"Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes","url":"https://arxiv.org/abs/2607.13071","canonical_url":"https://arxiv.org/abs/2607.13071","annotation":"Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","key_contribution":"Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","novelty":"Verification is promoted from a final check to a loop-control signal. Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","impact":"Use Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.13071; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"workspace;context;verification;state;exit","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Hiroki Tamba","publication_date":"2026-07-11","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"8 pages, companion to arXiv:2606.26185","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13071","date_added":"2026-07-17"},{"row_id":"ale-0522","title":"Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0","url":"https://arxiv.org/abs/2607.14004","canonical_url":"https://arxiv.org/abs/2607.14004","annotation":"Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.","key_contribution":"Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.","impact":"Use Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0 to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.14004; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification;budget","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Wenxiao Wang; Priyatham Kattakinda; Soheil Feizi","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Technical Report by RELAI (relai.ai)","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14004","date_added":"2026-07-17"},{"row_id":"ale-0523","title":"Does Multi-Agent Debate Improve AI Feedback on Research Papers?","url":"https://arxiv.org/abs/2607.14713","canonical_url":"https://arxiv.org/abs/2607.14713","annotation":"In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.","key_contribution":"In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.","impact":"Use Does Multi-Agent Debate Improve AI Feedback on Research Papers? to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.14713; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"delegation;verification;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Tomas Havranek; Zuzana Irsova","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"29 pages, 1 figure, 6 tables. Pre-registered on OSF; data, code, judge prompts, and blinded reports in the replication package on Zenodo. Project page: https://meta-analysis.cz/debate","primary_category":"econ.GN","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14713","date_added":"2026-07-17"},{"row_id":"ale-0524","title":"Awesome Harness Engineering by ai-boost","url":"https://github.com/ai-boost/awesome-harness-engineering","canonical_url":"https://github.com/ai-boost/awesome-harness-engineering","annotation":"Comprehensive list for the agent harness layer that Loop Engineering builds on.","key_contribution":"Comprehensive list for the agent harness layer that Loop Engineering builds on.","novelty":"Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Comprehensive list for the agent harness layer that Loop Engineering builds on.","impact":"Use Awesome Harness Engineering by ai-boost to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Inspectable GitHub source (3,111 stars; 333 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"List","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Adjacent Awesome Lists","section_slug":"adjacent-awesome-lists","lifecycle_stages":"whole-loop","audience":"builder","evidence_class":"curated-index","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-29","publication_year":"2026","publication_venue":"ai-boost/awesome-harness-engineering","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ai-boost/awesome-harness-engineering","github_stars":"3111","arxiv_id":"","date_added":""},{"row_id":"ale-0525","title":"Awesome Harness Engineering by walkinglabs","url":"https://github.com/walkinglabs/awesome-harness-engineering","canonical_url":"https://github.com/walkinglabs/awesome-harness-engineering","annotation":"High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.","key_contribution":"High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.","impact":"Use Awesome Harness Engineering by walkinglabs to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Inspectable GitHub source (3,635 stars; 295 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"List","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Adjacent Awesome Lists","section_slug":"adjacent-awesome-lists","lifecycle_stages":"context;verification","audience":"builder","evidence_class":"curated-index","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-29","publication_year":"2026","publication_venue":"walkinglabs/awesome-harness-engineering","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"walkinglabs/awesome-harness-engineering","github_stars":"3635","arxiv_id":"","date_added":""},{"row_id":"ale-0526","title":"Awesome Agent Harness","url":"https://github.com/AutoJunjie/awesome-agent-harness","canonical_url":"https://github.com/AutoJunjie/awesome-agent-harness","annotation":"Curated tools and resources for environments, constraints, and feedback around coding agents.","key_contribution":"Curated tools and resources for environments, constraints, and feedback around coding agents.","novelty":"Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Curated tools and resources for environments, constraints, and feedback around coding agents.","impact":"Use Awesome Agent Harness to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Inspectable GitHub source (488 stars; 46 forks; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"List","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Adjacent Awesome Lists","section_slug":"adjacent-awesome-lists","lifecycle_stages":"workspace","audience":"builder","evidence_class":"curated-index","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-05","publication_year":"2026","publication_venue":"AutoJunjie/awesome-agent-harness","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"AutoJunjie/awesome-agent-harness","github_stars":"488","arxiv_id":"","date_added":""},{"row_id":"ale-0527","title":"Awesome Context Engineering","url":"https://github.com/Meirtz/Awesome-Context-Engineering","canonical_url":"https://github.com/Meirtz/Awesome-Context-Engineering","annotation":"Survey-style list for context engineering across LLMs and agents.","key_contribution":"Survey-style list for context engineering across LLMs and agents.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Survey-style list for context engineering across LLMs and agents.","impact":"Use Awesome Context Engineering to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Inspectable GitHub source (3,239 stars; 258 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"List","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Adjacent Awesome Lists","section_slug":"adjacent-awesome-lists","lifecycle_stages":"context","audience":"builder","evidence_class":"curated-index","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-07-02","publication_year":"2025","publication_venue":"Meirtz/Awesome-Context-Engineering","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Meirtz/Awesome-Context-Engineering","github_stars":"3239","arxiv_id":"","date_added":""},{"row_id":"ale-0528","title":"Awesome Prompt Engineering","url":"https://github.com/promptslab/Awesome-Prompt-Engineering","canonical_url":"https://github.com/promptslab/Awesome-Prompt-Engineering","annotation":"Classic adjacent list for prompt techniques and prompting resources.","key_contribution":"Classic adjacent list for prompt techniques and prompting resources.","novelty":"Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Classic adjacent list for prompt techniques and prompting resources.","impact":"Use Awesome Prompt Engineering to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Inspectable GitHub source (6,172 stars; 728 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"List","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Adjacent Awesome Lists","section_slug":"adjacent-awesome-lists","lifecycle_stages":"whole-loop","audience":"builder","evidence_class":"curated-index","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-02-09","publication_year":"2023","publication_venue":"promptslab/Awesome-Prompt-Engineering","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"promptslab/Awesome-Prompt-Engineering","github_stars":"6172","arxiv_id":"","date_added":""},{"row_id":"ale-0529","title":"Awesome LLM Agents","url":"https://github.com/kaushikb11/awesome-llm-agents","canonical_url":"https://github.com/kaushikb11/awesome-llm-agents","annotation":"General list of LLM agent papers, frameworks, and applications.","key_contribution":"General list of LLM agent papers, frameworks, and applications.","novelty":"Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. General list of LLM agent papers, frameworks, and applications.","impact":"Use Awesome LLM Agents to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Inspectable GitHub source (1,533 stars; 329 forks; updated 2026-07-15); popularity is context, not proof of reliability.","resource_type":"List","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Adjacent Awesome Lists","section_slug":"adjacent-awesome-lists","lifecycle_stages":"whole-loop","audience":"builder","evidence_class":"curated-index","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-04-04","publication_year":"2023","publication_venue":"kaushikb11/awesome-llm-agents","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"kaushikb11/awesome-llm-agents","github_stars":"1533","arxiv_id":"","date_added":""},{"row_id":"ale-0530","title":"Awesome AI Agents","url":"https://github.com/e2b-dev/awesome-ai-agents","canonical_url":"https://github.com/e2b-dev/awesome-ai-agents","annotation":"Broad AI agent ecosystem map.","key_contribution":"Broad AI agent ecosystem map.","novelty":"Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. 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Addy Osmani's framing of loop engineering as the layer above manually prompting coding agents, with concrete primitives across Codex and Claude Code; also on [Substack](https://addyo.substack.com/p/loop-engineering) with the original discussion trail and Steinberger and Cherny quotations.","impact":"Use Loop Engineering by Addy Osmani to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"whole-loop","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Addy Osmani","publication_date":"","publication_year":"","publication_venue":"","publisher":"addyosmani.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0009","title":"Peter Steinberger on designing loops","url":"https://x.com/steipete/status/2063697162748260627","canonical_url":"https://x.com/steipete/status/2063697162748260627","annotation":"The June 2026 post - \"you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents\" - that catalyzed the current discussion.","key_contribution":"The June 2026 post - \"you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents\" - that catalyzed the current discussion.","novelty":"Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. The June 2026 post - \"you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents\" - that catalyzed the current discussion.","impact":"Use Peter Steinberger on designing loops to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"whole-loop","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-06-07","publication_year":"2026","publication_venue":"","publisher":"X (formerly Twitter)","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0010","title":"Boris Cherny: five tips for running Opus autonomously for hours or days","url":"https://x.com/bcherny/status/2063792263067754658","canonical_url":"https://x.com/bcherny/status/2063792263067754658","annotation":"The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","key_contribution":"The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","novelty":"The agent workflow includes explicit self-checking or gated completion. The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","impact":"Use Boris Cherny: five tips for running Opus autonomously for hours or days to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"objective;workspace;verification","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-06-08","publication_year":"2026","publication_venue":"","publisher":"X (formerly Twitter)","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0011","title":"Loop Engineering by Cobus Greyling","url":"https://cobusgreyling.substack.com/p/loop-engineering","canonical_url":"https://cobusgreyling.substack.com/p/loop-engineering","annotation":"Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","key_contribution":"Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","novelty":"State persistence is explicit enough for repeated runs and handoff. Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","impact":"Use Loop Engineering by Cobus Greyling to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from cobusgreyling.substack.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"intake;delegation;verification;state","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Cobus Greyling","publication_date":"","publication_year":"","publication_venue":"","publisher":"Substack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0012","title":"Stop Prompting. Design the Loop.","url":"https://www.pulumi.com/blog/stop-prompting-design-the-loop/","canonical_url":"https://www.pulumi.com/blog/stop-prompting-design-the-loop/","annotation":"Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","key_contribution":"Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","impact":"Use Stop Prompting. Design the Loop. to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from www.pulumi.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"workspace;context;delegation;verification;exit","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Engin Diri","publication_date":"2026-06-09","publication_year":"2026","publication_venue":"","publisher":"pulumi","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0013","title":"Writing Loops, Not Prompts, Explained","url":"https://rico.codes/loops-not-prompts","canonical_url":"https://rico.codes/loops-not-prompts","annotation":"Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","key_contribution":"Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","novelty":"Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","impact":"Use Writing Loops, Not Prompts, Explained to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from rico.codes; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"exit","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"rico.codes","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0014","title":"Loop Engineering: A Guide for Engineers and Practitioners","url":"https://medium.com/@adnanmasood/loop-engineering-a-guide-for-engineers-and-practitioners-893bb65ea943","canonical_url":"https://medium.com/@adnanmasood/loop-engineering-a-guide-for-engineers-and-practitioners-893bb65ea943","annotation":"Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","key_contribution":"Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","novelty":"The resource is directly reusable as a starting artifact. Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","impact":"Use Loop Engineering: A Guide for Engineers and Practitioners to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from medium.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"trigger;budget;exit","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Adnan Masood, PhD.","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"","publisher":"Medium","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0015","title":"Loop Engineering: When Generation Gets Cheap, Judgment Gets Expensive","url":"https://sderosiaux.substack.com/p/loop-engineering-cheap-generation","canonical_url":"https://sderosiaux.substack.com/p/loop-engineering-cheap-generation","annotation":"Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","key_contribution":"Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","novelty":"Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","impact":"Use Loop Engineering: When Generation Gets Cheap, Judgment Gets Expensive to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from sderosiaux.substack.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"whole-loop","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Stephane Derosiaux","publication_date":"","publication_year":"","publication_venue":"","publisher":"Substack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0016","title":"Andrew Ng on Loop Engineering and the Three Loops of AI-Native Product Development","url":"https://x.com/AndrewYNg/status/2071988145667928442","canonical_url":"https://x.com/AndrewYNg/status/2071988145667928442","annotation":"Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","key_contribution":"Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","novelty":"State persistence is explicit enough for repeated runs and handoff. Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","impact":"Use Andrew Ng on Loop Engineering and the Three Loops of AI-Native Product Development to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"state;escalation","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-06-30","publication_year":"2026","publication_venue":"","publisher":"X (formerly Twitter)","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0017","title":"From Prompting Agents to Loop Engineering","url":"https://x.com/omarsar0/status/2068008743153832264","canonical_url":"https://x.com/omarsar0/status/2068008743153832264","annotation":"DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.","key_contribution":"DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.","novelty":"Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.","impact":"Use From Prompting Agents to Loop Engineering to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"exit","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-06-19","publication_year":"2026","publication_venue":"","publisher":"X (formerly Twitter)","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0018","title":"My Lord! AI Programming Undergoes Another Major Shift","url":"https://eu.36kr.com/en/p/3844224911346184","canonical_url":"https://eu.36kr.com/en/p/3844224911346184","annotation":"Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","key_contribution":"Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","novelty":"State persistence is explicit enough for repeated runs and handoff. Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","impact":"Use My Lord! AI Programming Undergoes Another Major Shift to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from eu.36kr.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"state","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"eu.36kr.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0019","title":"The Anthropic leader who built Claude Code ditched prompting - now he writes loops","url":"https://thenewstack.io/loop-engineering/","canonical_url":"https://thenewstack.io/loop-engineering/","annotation":"The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.","key_contribution":"The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.","novelty":"Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.","impact":"Use The Anthropic leader who built Claude Code ditched prompting - now he writes loops to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from thenewstack.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"whole-loop","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Janakiram MSV","publication_date":"2026-06-10","publication_year":"2026","publication_venue":"","publisher":"The New Stack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0020","title":"Engineering for Agents That Never Sleep","url":"https://nader.substack.com/p/engineering-for-agents-that-never","canonical_url":"https://nader.substack.com/p/engineering-for-agents-that-never","annotation":"Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","key_contribution":"Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","novelty":"Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","impact":"Use Engineering for Agents That Never Sleep to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from nader.substack.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"trigger;verification;escalation","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Nader Dabit","publication_date":"","publication_year":"","publication_venue":"","publisher":"Substack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0021","title":"Loop Engineering Orange Book","url":"https://github.com/alchaincyf/loop-engineering-orange-book","canonical_url":"https://github.com/alchaincyf/loop-engineering-orange-book","annotation":"Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","key_contribution":"Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","novelty":"The resource is directly reusable as a starting artifact. Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","impact":"Use Loop Engineering Orange Book to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Inspectable GitHub source (1,025 stars; 99 forks; NOASSERTION license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"whole-loop","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-15","publication_year":"2026","publication_venue":"alchaincyf/loop-engineering-orange-book","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"alchaincyf/loop-engineering-orange-book","github_stars":"1025","arxiv_id":"","date_added":""},{"row_id":"ale-0022","title":"How I AI: How to Write AI Agent Loops in Claude Code and Codex","url":"https://www.lennysnewsletter.com/p/how-i-ai-how-to-write-ai-agent-loops","canonical_url":"https://www.lennysnewsletter.com/p/how-i-ai-how-to-write-ai-agent-loops","annotation":"Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","key_contribution":"Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","novelty":"The trigger or cadence is explicit, making the workflow recurring rather than one-off. Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","impact":"Use How I AI: How to Write AI Agent Loops in Claude Code and Codex to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from www.lennysnewsletter.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"objective;trigger;delegation","audience":"newcomer","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Lenny Rachitsky","publication_date":"","publication_year":"","publication_venue":"","publisher":"lennysnewsletter.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0023","title":"Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control","url":"https://arxiv.org/abs/2607.14890","canonical_url":"https://arxiv.org/abs/2607.14890","annotation":"Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","key_contribution":"Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","impact":"Use Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.14890; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Start Here","section_slug":"start-here","lifecycle_stages":"verification;exit","audience":"newcomer;researcher;evaluator","loop_layer":"cross-layer","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jek Huang; Jeffery Hsia; Jiayi Sun; Freddie Shi; Wei Huang; Ian H. White","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"48 pages, 10 figures, 29 numbered tables. Preprint v1","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14890","date_added":"2026-07-17"},{"row_id":"ale-0024","title":"PR babysitter","url":"patterns/pr-babysitter.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/pr-babysitter.md","annotation":"Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","key_contribution":"Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","impact":"Use PR babysitter to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"whole-loop","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0025","title":"CI repair loop","url":"patterns/ci-repair-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/ci-repair-loop.md","annotation":"Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","key_contribution":"Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","impact":"Use CI repair loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"escalation","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0026","title":"Docs drift collector","url":"patterns/docs-drift-collector.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/docs-drift-collector.md","annotation":"Finds mismatches between docs and code, proposes small patches, and verifies examples.","key_contribution":"Finds mismatches between docs and code, proposes small patches, and verifies examples.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Finds mismatches between docs and code, proposes small patches, and verifies examples.","impact":"Use Docs drift collector to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"verification","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0027","title":"Deploy verifier","url":"patterns/deploy-verifier.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/deploy-verifier.md","annotation":"Watches rollout signals, compares them with release expectations, and stops on anomalies.","key_contribution":"Watches rollout signals, compares them with release expectations, and stops on anomalies.","novelty":"Verification is promoted from a final check to a loop-control signal. Watches rollout signals, compares them with release expectations, and stops on anomalies.","impact":"Use Deploy verifier to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"exit","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0028","title":"Feedback clusterer","url":"patterns/feedback-clusterer.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/feedback-clusterer.md","annotation":"Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","key_contribution":"Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","impact":"Use Feedback clusterer to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"whole-loop","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0029","title":"Dependency triage loop","url":"patterns/dependency-triage-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/dependency-triage-loop.md","annotation":"Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","key_contribution":"Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","impact":"Use Dependency triage loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"intake;verification;escalation","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0030","title":"Evaluation regression loop","url":"patterns/evaluation-regression-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/evaluation-regression-loop.md","annotation":"Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","key_contribution":"Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","impact":"Use Evaluation regression loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"verification","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0031","title":"Benchmark optimization loop","url":"patterns/benchmark-optimization-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/benchmark-optimization-loop.md","annotation":"Runs bounded experiments against a frozen benchmark and accepts only reproducible gains with correctness intact.","key_contribution":"Runs bounded experiments against a frozen benchmark and accepts only reproducible gains with correctness intact.","novelty":"The work turns loop quality into a measurable task or score. Runs bounded experiments against a frozen benchmark and accepts only reproducible gains with correctness intact.","impact":"Use Benchmark optimization loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"verification","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0032","title":"Security review loop","url":"patterns/security-review-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/security-review-loop.md","annotation":"Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","key_contribution":"Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","impact":"Use Security review loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"workspace;escalation","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0033","title":"Adversarial red-team loop","url":"patterns/adversarial-red-team-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/adversarial-red-team-loop.md","annotation":"Discovers agent failures inside an authorized sandbox, then independently reproduces, minimizes, and reports them.","key_contribution":"Discovers agent failures inside an authorized sandbox, then independently reproduces, minimizes, and reports them.","novelty":"Execution isolation and permission boundaries are part of the design. Discovers agent failures inside an authorized sandbox, then independently reproduces, minimizes, and reports them.","impact":"Use Adversarial red-team loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"intake;workspace","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0034","title":"Accessibility regression loop","url":"patterns/accessibility-regression-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/accessibility-regression-loop.md","annotation":"Repairs reproducible accessibility regressions while preserving required human review for non-automatable criteria.","key_contribution":"Repairs reproducible accessibility regressions while preserving required human review for non-automatable criteria.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Repairs reproducible accessibility regressions while preserving required human review for non-automatable criteria.","impact":"Use Accessibility regression loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"escalation","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0035","title":"Cost-control loop","url":"patterns/cost-control-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/cost-control-loop.md","annotation":"Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","key_contribution":"Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. 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Profiles a measured regression and verifies a narrow fix against the same controlled workload and correctness gates.","impact":"Use Performance regression loop to turn a recurring-agent idea into an explicit loop contract.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Pattern","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Pattern Library","section_slug":"pattern-library","lifecycle_stages":"verification","audience":"builder","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0037","title":"Bug hunting loop","url":"patterns/bug-hunting-loop.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/bug-hunting-loop.md","annotation":"Discovers, reproduces, minimizes, and reports bugs with concrete evidence.","key_contribution":"Discovers, reproduces, minimizes, and reports bugs with concrete evidence.","novelty":"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. 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Claude Code skill system for reusable loop instructions and assets.","impact":"Use Extend Claude with skills to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from code.claude.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude Code Docs","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0084","title":"Create custom subagents","url":"https://code.claude.com/docs/en/sub-agents","canonical_url":"https://code.claude.com/docs/en/sub-agents","annotation":"Claude Code custom subagents with isolated context, model choice, and tool permissions.","key_contribution":"Claude Code custom subagents with isolated context, model choice, and tool permissions.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Claude Code custom subagents with isolated context, model choice, and tool permissions.","impact":"Use Create custom subagents to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from code.claude.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude Code Docs","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0085","title":"Writing effective tools for AI agents","url":"https://www.anthropic.com/engineering/writing-tools-for-agents","canonical_url":"https://www.anthropic.com/engineering/writing-tools-for-agents","annotation":"Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.","key_contribution":"Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.","impact":"Use Writing effective tools for AI agents to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from www.anthropic.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0086","title":"Introducing advanced tool use on the Claude Developer Platform","url":"https://www.anthropic.com/engineering/advanced-tool-use?e45d281a_page=3","canonical_url":"https://www.anthropic.com/engineering/advanced-tool-use?e45d281a_page=3","annotation":"Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","key_contribution":"Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","impact":"Use Introducing advanced tool use on the Claude Developer Platform to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from www.anthropic.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0087","title":"Effective harnesses for long-running agents","url":"https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents","canonical_url":"https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents","annotation":"Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","key_contribution":"Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","impact":"Use Effective harnesses for long-running agents to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from www.anthropic.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"context;verification","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0088","title":"Claude Code best practices","url":"https://code.claude.com/docs/en/best-practices","canonical_url":"https://code.claude.com/docs/en/best-practices","annotation":"Widely cited workflow guidance that underlies many recurring Claude Code loops.","key_contribution":"Widely cited workflow guidance that underlies many recurring Claude Code loops.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Widely cited workflow guidance that underlies many recurring Claude Code loops.","impact":"Use Claude Code best practices to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from code.claude.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude Code Docs","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0089","title":"Claude Managed Agents: Scheduled Deployments and Vaults","url":"https://claude.com/blog/whats-new-in-claude-managed-agents","canonical_url":"https://claude.com/blog/whats-new-in-claude-managed-agents","annotation":"Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","key_contribution":"Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","novelty":"The trigger or cadence is explicit, making the workflow recurring rather than one-off. Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","impact":"Use Claude Managed Agents: Scheduled Deployments and Vaults to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from claude.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"trigger;workspace","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0090","title":"Getting Started with Loops","url":"https://claude.com/blog/getting-started-with-loops","canonical_url":"https://claude.com/blog/getting-started-with-loops","annotation":"Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.","key_contribution":"Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.","novelty":"Primary-source operational guidance rather than commentary. Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.","impact":"Use Getting Started with Loops to choose an implementation surface for repeatable agent work.","signal":"Contextual source from claude.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"objective;trigger;verification;budget;exit","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0091","title":"Claude Code What's New, Week 28","url":"https://code.claude.com/docs/en/whats-new/2026-w28","canonical_url":"https://code.claude.com/docs/en/whats-new/2026-w28","annotation":"Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.","key_contribution":"Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.","impact":"Use Claude Code What's New, Week 28 to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from code.claude.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"Claude Code Docs","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0092","title":"GitHub Agentic Workflows","url":"https://github.github.com/gh-aw/","canonical_url":"https://github.github.com/gh-aw/","annotation":"Repository automation that runs coding agents in GitHub Actions on events or schedules with guardrails.","key_contribution":"Repository automation that runs coding agents in GitHub Actions on events or schedules with guardrails.","novelty":"The trigger or cadence is explicit, making the workflow recurring rather than one-off. 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GitHub Next's umbrella framing for CI/CD-style AI automation across the software lifecycle, the category that agentic workflows demonstrate.","impact":"Use Continuous AI to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from githubnext.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"githubnext.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0094","title":"Automate repository tasks with GitHub Agentic Workflows","url":"https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows/","canonical_url":"https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows/","annotation":"Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/).","key_contribution":"Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/).","novelty":"Primary-source operational guidance rather than commentary. 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General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.","impact":"Use GitHub Copilot for Jira Is Now Generally Available to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from github.blog; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"intake;delegation","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"The GitHub Blog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0099","title":"Copilot Agent Session Streaming (Public Preview)","url":"https://github.blog/changelog/2026-07-02-copilot-agent-session-streaming-is-now-in-public-preview/","canonical_url":"https://github.blog/changelog/2026-07-02-copilot-agent-session-streaming-is-now-in-public-preview/","annotation":"Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.","key_contribution":"Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.","impact":"Use Copilot Agent Session Streaming (Public Preview) to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from github.blog; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;delegation","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"The GitHub Blog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0100","title":"Security Reviews in the GitHub Copilot App","url":"https://github.blog/changelog/2026-07-14-security-reviews-now-available-in-the-github-copilot-app","canonical_url":"https://github.blog/changelog/2026-07-14-security-reviews-now-available-in-the-github-copilot-app/","annotation":"Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","key_contribution":"Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","impact":"Use Security Reviews in the GitHub Copilot App to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from github.blog; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"The GitHub Blog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0101","title":"Cursor cloud agents","url":"https://cursor.com/docs/cloud-agent","canonical_url":"https://cursor.com/docs/cloud-agent","annotation":"Remote agents that work asynchronously in isolated environments and hand results back for review.","key_contribution":"Remote agents that work asynchronously in isolated environments and hand results back for review.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Remote agents that work asynchronously in isolated environments and hand results back for review.","impact":"Use Cursor cloud agents to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from cursor.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor Documentation","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0102","title":"Cursor 3.8: Improvements to Cursor Automations","url":"https://cursor.com/changelog/06-18-26","canonical_url":"https://cursor.com/changelog/06-18-26","annotation":"Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","key_contribution":"Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","impact":"Use Cursor 3.8: Improvements to Cursor Automations to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from cursor.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"trigger;workspace","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0103","title":"Expanding Our Long-Running Agents Research Preview","url":"https://cursor.com/blog/long-running-agents","canonical_url":"https://cursor.com/blog/long-running-agents","annotation":"Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","key_contribution":"Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","impact":"Use Expanding Our Long-Running Agents Research Preview to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"escalation","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Cursor Team","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0104","title":"Cursor 3.11: Side Chats, Transcript Search, and Cloud Agent Hooks","url":"https://cursor.com/changelog/side-chat","canonical_url":"https://cursor.com/changelog/side-chat","annotation":"Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","key_contribution":"Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","impact":"Use Cursor 3.11: Side Chats, Transcript Search, and Cloud Agent Hooks to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from cursor.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"delegation;exit","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0105","title":"Jules","url":"https://jules.google/docs","canonical_url":"https://jules.google/docs","annotation":"Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","key_contribution":"Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","impact":"Use Jules to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from jules.google; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Jules","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0106","title":"Devin Docs","url":"https://docs.devin.ai/get-started/devin-intro","canonical_url":"https://docs.devin.ai/get-started/devin-intro","annotation":"Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","key_contribution":"Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","impact":"Use Devin Docs to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from docs.devin.ai; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Devin Docs","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0107","title":"Amp: Agents, Anywhere","url":"https://ampcode.com/news/agents-anywhere","canonical_url":"https://ampcode.com/news/agents-anywhere","annotation":"Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.","key_contribution":"Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.","novelty":"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.","impact":"Use Amp: Agents, Anywhere to choose an implementation surface for repeatable agent work.","signal":"Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Official Runtime Guides","section_slug":"official-runtime-guides","lifecycle_stages":"workspace;context;delegation;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"ampcode.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0108","title":"ReAct: Synergizing Reasoning and Acting in Language Models","url":"https://arxiv.org/abs/2210.03629","canonical_url":"https://openreview.net/forum?id=WE_vluYUL-X","annotation":"Foundational reason-act-observe loop for tool-using language agents.","key_contribution":"Foundational reason-act-observe loop for tool-using language agents.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Foundational reason-act-observe loop for tool-using language agents.","impact":"Use ReAct: Synergizing Reasoning and Acting in Language Models to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2210.03629; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"workspace","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Shunyu Yao; Jeffrey Zhao; Dian Yu; Nan Du; Izhak Shafran; Karthik Narasimhan; Yuan Cao","publication_date":"2023","publication_year":"2023","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"OpenReview proceedings record","github_repo":"","github_stars":"","arxiv_id":"2210.03629","date_added":""},{"row_id":"ale-0109","title":"Reflexion: Language Agents with Verbal Reinforcement Learning","url":"https://arxiv.org/abs/2303.11366","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2023/hash/1b44b878bb782e6954cd888628510e90-Abstract-Conference.html","annotation":"Converts environment feedback into written reflections stored in memory for future attempts.","key_contribution":"Converts environment feedback into written reflections stored in memory for future attempts.","novelty":"Persistent memory is treated as an external runtime artifact. Converts environment feedback into written reflections stored in memory for future attempts.","impact":"Use Reflexion: Language Agents with Verbal Reinforcement Learning to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2303.11366; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"context","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Noah Shinn; Federico Cassano; Edward Berman; Ashwin Gopinath; Karthik Narasimhan; Shunyu Yao","publication_date":"2023","publication_year":"2023","publication_venue":"Advances in Neural Information Processing Systems 36 (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"10.52202/075280-0377","publication_note":"Published in Advances in Neural Information Processing Systems 36 (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"NeurIPS proceedings and DOI records","github_repo":"","github_stars":"","arxiv_id":"2303.11366","date_added":""},{"row_id":"ale-0110","title":"Self-Refine: Iterative Refinement with Self-Feedback","url":"https://arxiv.org/abs/2303.17651","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2023/hash/91edff07232fb1b55a505a9e9f6c0ff3-Abstract-Conference.html","annotation":"Generate-feedback-refine loop where a model improves outputs over repeated passes.","key_contribution":"Generate-feedback-refine loop where a model improves outputs over repeated passes.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Generate-feedback-refine loop where a model improves outputs over repeated passes.","impact":"Use Self-Refine: Iterative Refinement with Self-Feedback to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2303.17651; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Aman Madaan; Niket Tandon; Prakhar Gupta; Skyler Hallinan; Luyu Gao; Sarah Wiegreffe; Uri Alon; Nouha Dziri; Shrimai Prabhumoye; Yiming Yang; Shashank Gupta; Bodhisattwa Prasad Majumder; Katherine Hermann; Sean Welleck; Amir Yazdanbakhsh; Peter Clark","publication_date":"2023","publication_year":"2023","publication_venue":"Advances in Neural Information Processing Systems 36 (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"","publication_note":"Published in Advances in Neural Information Processing Systems 36 (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"NeurIPS proceedings record","github_repo":"","github_stars":"","arxiv_id":"2303.17651","date_added":""},{"row_id":"ale-0111","title":"CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing","url":"https://arxiv.org/abs/2305.11738","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/fef126561bbf9d4467dbb8d27334b8fe-Abstract-Conference.html","annotation":"Uses tools to ground critique and correction rather than relying only on introspection.","key_contribution":"Uses tools to ground critique and correction rather than relying only on introspection.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Uses tools to ground critique and correction rather than relying only on introspection.","impact":"Use CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2305.11738; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Zhibin Gou; Zhihong Shao; Yeyun Gong; Yelong Shen; Yujiu Yang; Nan Duan; Weizhu Chen","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2305.11738","date_added":""},{"row_id":"ale-0112","title":"Tree of Thoughts","url":"https://arxiv.org/abs/2305.10601","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2023/hash/271db9922b8d1f4dd7aaef84ed5ac703-Abstract.html","annotation":"Search over multiple reasoning branches; relevant when loop design needs exploration before committing.","key_contribution":"Search over multiple reasoning branches; relevant when loop design needs exploration before committing.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Search over multiple reasoning branches; relevant when loop design needs exploration before committing.","impact":"Use Tree of Thoughts to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2305.10601; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Shunyu Yao; Dian Yu; Jeffrey Zhao; Izhak Shafran; Thomas L. Griffiths; Yuan Cao; Karthik Narasimhan","publication_date":"2023","publication_year":"2023","publication_venue":"Advances in Neural Information Processing Systems 36 (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"","publication_note":"Published in Advances in Neural Information Processing Systems 36 (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"NeurIPS proceedings record","github_repo":"","github_stars":"","arxiv_id":"2305.10601","date_added":""},{"row_id":"ale-0113","title":"Graph of Thoughts","url":"https://arxiv.org/abs/2308.09687","canonical_url":"https://ojs.aaai.org/index.php/AAAI/article/view/29720","annotation":"Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","key_contribution":"Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","impact":"Use Graph of Thoughts to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2308.09687; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Maciej Besta; Nils Blach; Ales Kubicek; Robert Gerstenberger; Michal Podstawski; Lukas Gianinazzi; Joanna Gajda; Tomasz Lehmann; Hubert Niewiadomski; Piotr Nyczyk; Torsten Hoefler","publication_date":"2024-03-24","publication_year":"2024","publication_venue":"Proceedings of the AAAI Conference on Artificial Intelligence 38 (AAAI)","publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","doi":"10.1609/aaai.v38i16.29720","publication_note":"Published in Proceedings of the AAAI Conference on Artificial Intelligence 38 (AAAI); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"AAAI proceedings and DOI records","github_repo":"","github_stars":"","arxiv_id":"2308.09687","date_added":""},{"row_id":"ale-0114","title":"Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models","url":"https://arxiv.org/abs/2310.04406","canonical_url":"https://proceedings.mlr.press/v235/zhou24r.html","annotation":"Combines search, action, and environment feedback for language agents.","key_contribution":"Combines search, action, and environment feedback for language agents.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Combines search, action, and environment feedback for language agents.","impact":"Use Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2310.04406; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Andy Zhou; Kai Yan; Michal Shlapentokh-Rothman; Haohan Wang; Yu-Xiong Wang","publication_date":"2024","publication_year":"2024","publication_venue":"Proceedings of the 41st International Conference on Machine Learning (ICML)","publisher":"PMLR","doi":"","publication_note":"Published in Proceedings of the 41st International Conference on Machine Learning (ICML); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"PMLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2310.04406","date_added":""},{"row_id":"ale-0115","title":"Voyager: An Open-Ended Embodied Agent with Large Language Models","url":"https://arxiv.org/abs/2305.16291","canonical_url":"https://openreview.net/forum?id=ehfRiF0R3a","annotation":"Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","key_contribution":"Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","impact":"Use Voyager: An Open-Ended Embodied Agent with Large Language Models to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2305.16291; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Guanzhi Wang; Yuqi Xie; Yunfan Jiang; Ajay Mandlekar; Chaowei Xiao; Yuke Zhu; Linxi Fan; Anima Anandkumar","publication_date":"2024","publication_year":"2024","publication_venue":"Transactions on Machine Learning Research (TMLR)","publisher":"OpenReview","doi":"","publication_note":"Published in Transactions on Machine Learning Research (TMLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"TMLR OpenReview record","github_repo":"","github_stars":"","arxiv_id":"2305.16291","date_added":""},{"row_id":"ale-0116","title":"Generative Agents: Interactive Simulacra of Human Behavior","url":"https://arxiv.org/abs/2304.03442","canonical_url":"https://doi.org/10.1145/3586183.3606763","annotation":"Introduces reflection and memory mechanisms for long-running agent behavior.","key_contribution":"Introduces reflection and memory mechanisms for long-running agent behavior.","novelty":"Persistent memory is treated as an external runtime artifact. Introduces reflection and memory mechanisms for long-running agent behavior.","impact":"Use Generative Agents: Interactive Simulacra of Human Behavior to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2304.03442; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"context;escalation","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Joon Sung Park; Joseph C. O'Brien; Carrie J. Cai; Meredith Ringel Morris; Percy Liang; Michael S. Bernstein","publication_date":"2023-10-29","publication_year":"2023","publication_venue":"Proceedings of the 36th ACM Symposium on User Interface Software and Technology (UIST)","publisher":"Association for Computing Machinery","doi":"10.1145/3586183.3606763","publication_note":"Published in Proceedings of the 36th ACM Symposium on User Interface Software and Technology (UIST); the linked arXiv record remains available for open access.","primary_category":"cs.HC","metadata_source":"ACM DOI record","github_repo":"","github_stars":"","arxiv_id":"2304.03442","date_added":""},{"row_id":"ale-0117","title":"Measuring AI Ability to Complete Long Software Tasks","url":"https://arxiv.org/abs/2503.14499","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2025/hash/85069585133c4c168c865e65d72e9775-Abstract-Conference.html","annotation":"METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","key_contribution":"METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","impact":"Use Measuring AI Ability to Complete Long Software Tasks to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2503.14499; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"state;budget;escalation","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Thomas Kwa; Ben West; Joel Becker; Amy Deng; Katharyn Garcia; Max Hasin; Sami Jawhar; Megan Kinniment; Nate Rush; Sydney Von Arx; Ryan Bloom; Thomas Broadley; Haoxing Du; Brian Goodrich; Nikola Jurkovic; Luke Harold Miles; Seraphina Nix; Tao Lin; Chris Painter; Neev Parikh; David Rein; Lucas Jun Koba Sato; Hjalmar Wijk; Daniel M. Ziegler; Elizabeth Barnes; Lawrence Chan","publication_date":"2025","publication_year":"2025","publication_venue":"Advances in Neural Information Processing Systems 38 (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"","publication_note":"Published in Advances in Neural Information Processing Systems 38 (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"NeurIPS proceedings record","github_repo":"","github_stars":"","arxiv_id":"2503.14499","date_added":""},{"row_id":"ale-0118","title":"Measuring AI Ability to Complete Long Tasks","url":"https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/","canonical_url":"https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/","annotation":"Accessible summary of the 50% task-completion time horizon and its doubling trend.","key_contribution":"Accessible summary of the 50% task-completion time horizon and its doubling trend.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Accessible summary of the 50% task-completion time horizon and its doubling trend.","impact":"Use Measuring AI Ability to Complete Long Tasks to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"exit","audience":"builder","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2025-03-19","publication_year":"2025","publication_venue":"METR Blog","publisher":"metr.org","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0119","title":"Reflection-Driven Control for Trustworthy Code Agents","url":"https://arxiv.org/abs/2512.21354","canonical_url":"https://openreview.net/forum?id=vUtz66IHD1","annotation":"Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.","key_contribution":"Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.","impact":"Use Reflection-Driven Control for Trustworthy Code Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2512.21354; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Bin Wang; Jiazheng Quan; Xingrui Yu; Hansen Hu; Yuhao; Ivor Tsang","publication_date":"2026","publication_year":"2026","publication_venue":"AAAI Workshop on Trust and Control in Agentic AI (TrustAgent)","publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","doi":"","publication_note":"Published in AAAI Workshop on Trust and Control in Agentic AI (TrustAgent); the linked arXiv record remains available for open access.","primary_category":"cs.CR","metadata_source":"AAAI workshop OpenReview record","github_repo":"","github_stars":"","arxiv_id":"2512.21354","date_added":""},{"row_id":"ale-0120","title":"Hyperagents","url":"https://arxiv.org/abs/2603.19461","canonical_url":"https://arxiv.org/abs/2603.19461","annotation":"Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","key_contribution":"Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","impact":"Use Hyperagents to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2603.19461; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jenny Zhang; Bingchen Zhao; Wannan Yang; Jakob Foerster; Jeff Clune; Minqi Jiang; Sam Devlin; Tatiana Shavrina","publication_date":"2026-03-19","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code at https://github.com/facebookresearch/Hyperagents","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.19461","date_added":""},{"row_id":"ale-0121","title":"PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks","url":"https://arxiv.org/abs/2512.03549","canonical_url":"https://arxiv.org/abs/2512.03549","annotation":"Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.","key_contribution":"Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.","novelty":"The work targets tasks that exceed a single context window or prompt session. Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.","impact":"Use PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2512.03549; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yuki Orimo; Iori Kurata; Hodaka Mori; Ryuhei Okuno; Ryohto Sawada; Daisuke Okanohara","publication_date":"2025-12-03","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2512.03549","date_added":""},{"row_id":"ale-0122","title":"When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents","url":"https://arxiv.org/abs/2603.17104","canonical_url":"https://arxiv.org/abs/2603.17104","annotation":"Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","key_contribution":"Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","novelty":"The work targets tasks that exceed a single context window or prompt session. Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","impact":"Use When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2603.17104; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Lu Yan; Xuan Chen; Xiangyu Zhang","publication_date":"2026-03-17","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.17104","date_added":""},{"row_id":"ale-0123","title":"Reflexion code","url":"https://github.com/noahshinn/reflexion","canonical_url":"https://github.com/noahshinn/reflexion","annotation":"Reference implementation and experiments for verbal reinforcement loops.","key_contribution":"Reference implementation and experiments for verbal reinforcement loops.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Reference implementation and experiments for verbal reinforcement loops.","impact":"Use Reflexion code to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Inspectable GitHub source (3,205 stars; 312 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"builder","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-03-22","publication_year":"2023","publication_venue":"noahshinn/reflexion","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"noahshinn/reflexion","github_stars":"3205","arxiv_id":"","date_added":""},{"row_id":"ale-0124","title":"Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting","url":"https://arxiv.org/abs/2607.00038","canonical_url":"https://arxiv.org/abs/2607.00038","annotation":"Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","key_contribution":"Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","novelty":"Verification is promoted from a final check to a loop-control signal. Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","impact":"Use Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.00038; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"objective;trigger;context;verification;exit","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Sandeco Macedo","publication_date":"2026-06-28","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00038","date_added":""},{"row_id":"ale-0125","title":"From Question Answering to Task Completion: A Survey on Agent System and Harness Design","url":"https://arxiv.org/abs/2606.20683","canonical_url":"https://arxiv.org/abs/2606.20683","annotation":"Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","key_contribution":"Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","novelty":"Verification is promoted from a final check to a loop-control signal. Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","impact":"Use From Question Answering to Task Completion: A Survey on Agent System and Harness Design to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2606.20683; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"context;verification;state;exit","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jianyuan Guo; Zhiwei Hao; Chengcheng Wang; Cheng Fan; Tingzhang Luo; Hongguang Li; Ying Gao; Hefei Mei; Jiankun Peng; Rongjian Xu; Minjing Dong; Han Wu; Mengyu Zheng; Kai Han; Shiqi Wang; Chang Xu; Yunhe Wang","publication_date":"2026-06-14","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.20683","date_added":""},{"row_id":"ale-0126","title":"MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems","url":"https://arxiv.org/abs/2605.22794","canonical_url":"https://arxiv.org/abs/2605.22794","annotation":"Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","key_contribution":"Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","impact":"Use MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2605.22794; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification;state;escalation","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Qianshu Cai; Yonggang Zhang; Xianzhang Jia; Huajiang Zheng; Wei Xue; Jun Song; Xinmei Tian; Yike Guo","publication_date":"2026-05-21","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"12 pages, 3 figures, 2 tables. Preprint. Code: https://github.com/hkgai-official/Moss","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.22794","date_added":""},{"row_id":"ale-0127","title":"METR Time Horizon 1.1","url":"https://metr.org/blog/2026-1-29-time-horizon-1-1/","canonical_url":"https://metr.org/blog/2026-1-29-time-horizon-1-1/","annotation":"Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","key_contribution":"Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","impact":"Use METR Time Horizon 1.1 to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"builder","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-01-29","publication_year":"2026","publication_venue":"METR Blog","publisher":"metr.org","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0128","title":"MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution","url":"https://arxiv.org/abs/2607.05297","canonical_url":"https://arxiv.org/abs/2607.05297","annotation":"Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","key_contribution":"Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","impact":"Use MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.05297; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Zefeng Wang; Minxi Yan; Jinhe Bi; Sikuan Yan; Volker Tresp; Yunpu Ma","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05297","date_added":""},{"row_id":"ale-0129","title":"SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe","url":"https://arxiv.org/abs/2607.03451","canonical_url":"https://arxiv.org/abs/2607.03451","annotation":"Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","key_contribution":"Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","impact":"Use SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.03451; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yifei Shen; Bo Li; Xinjie Zhang","publication_date":"2026-07-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.03451","date_added":""},{"row_id":"ale-0130","title":"Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops","url":"https://arxiv.org/abs/2607.07663","canonical_url":"https://arxiv.org/abs/2607.07663","annotation":"Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","key_contribution":"Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","impact":"Use Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07663; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Mingguang Chen; Licheng Wang; Bo Qu","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"42 pages, 6 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07663","date_added":""},{"row_id":"ale-0131","title":"From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents","url":"https://arxiv.org/abs/2607.07321","canonical_url":"https://arxiv.org/abs/2607.07321","annotation":"EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","key_contribution":"EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","impact":"Use From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07321; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Haipeng Ding; Yuexiang Xie; Zhewei Wei; Yaliang Li; Bolin Ding","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07321","date_added":""},{"row_id":"ale-0132","title":"TTHE: Test-Time Harness Evolution","url":"https://arxiv.org/abs/2607.08124","canonical_url":"https://arxiv.org/abs/2607.08124","annotation":"Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","key_contribution":"Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","impact":"Use TTHE: Test-Time Harness Evolution to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.08124; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jun Nie; Yonggang Zhang; Jun Song; Qianshu Cai; Dahai Yu; Yike Guo; Xinmei Tian; Bo Han","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"15 pages, 5 figures","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08124","date_added":""},{"row_id":"ale-0133","title":"DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment","url":"https://arxiv.org/abs/2607.07820","canonical_url":"https://arxiv.org/abs/2607.07820","annotation":"Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","key_contribution":"Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","novelty":"Verification is promoted from a final check to a loop-control signal. Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","impact":"Use DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07820; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xinyu Geng; Xuanhua He; Sixiang Chen; Yanjing Xiao; Fan Zhang; Shijue Huang; Haitao Mi; Zhenwen Liang; Tianqing Fang; Yi R. Fung","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07820","date_added":""},{"row_id":"ale-0134","title":"What Makes a Good Bug Report for an AI Agent?","url":"https://arxiv.org/abs/2607.07593","canonical_url":"https://arxiv.org/abs/2607.07593","annotation":"Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","key_contribution":"Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","impact":"Use What Makes a Good Bug Report for an AI Agent? to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07593; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"intake","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Lara Khatib; Noble Saji Mathews; Meiyappan Nagappan; Pengyu Nie; Thomas Zimmermann","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07593","date_added":""},{"row_id":"ale-0135","title":"AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution","url":"https://arxiv.org/abs/2607.08252","canonical_url":"https://arxiv.org/abs/2607.08252","annotation":"Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","key_contribution":"Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","novelty":"State persistence is explicit enough for repeated runs and handoff. Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","impact":"Use AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.08252; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification;state","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Mengchen Li","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"52 pages, 13 figures/tables, ancillary public-safe evaluation artifacts included","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08252","date_added":""},{"row_id":"ale-0136","title":"Agentic Data Environments","url":"https://arxiv.org/abs/2607.07397","canonical_url":"http://sites.computer.org/debull/A26mar/A26MAR-CD.pdf#page=7","annotation":"Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","key_contribution":"Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","novelty":"State persistence is explicit enough for repeated runs and handoff. Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","impact":"Use Agentic Data Environments to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07397; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"state;budget","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Elaine Ang; Chenxi Huang; Georgios Liargkovas; Jerry Liu; Jinhui Liu; Nikos Pagonas; Charlie Summers; Haonan Wang; Jiakai Xu; Tianle Zhou; Yusen Zhang; Zhou Yu; Zhuo Zhang; Tianyi Peng; Kostis Kaffes; Eugene Wu","publication_date":"2026-03","publication_year":"2026","publication_venue":"IEEE Data Engineering Bulletin 50(1)","publisher":"IEEE","doi":"","publication_note":"Published in IEEE Data Engineering Bulletin 50(1); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"IEEE Data Engineering Bulletin record","github_repo":"","github_stars":"","arxiv_id":"2607.07397","date_added":""},{"row_id":"ale-0137","title":"Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation","url":"https://arxiv.org/abs/2607.08938","canonical_url":"https://arxiv.org/abs/2607.08938","annotation":"Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","key_contribution":"Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","impact":"Use Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.08938; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"budget","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Chenyang Yang; Xinran Zhao; Tongshuang Wu; Christian Kästner","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08938","date_added":""},{"row_id":"ale-0138","title":"Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills","url":"https://arxiv.org/abs/2607.09065","canonical_url":"https://arxiv.org/abs/2607.09065","annotation":"First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","key_contribution":"First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","impact":"Use Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.09065; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jialun Cao; Xinru Yan; Songqiang Chen; Yaojie Lu; Zhongxin Liu; Shing-Chi Cheung","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09065","date_added":""},{"row_id":"ale-0139","title":"Harness Engineering for Self-Improvement","url":"https://lilianweng.github.io/posts/2026-07-04-harness/","canonical_url":"https://lilianweng.github.io/posts/2026-07-04-harness/","annotation":"Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","key_contribution":"Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","novelty":"Orchestration and control flow are made explicit and inspectable. Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","impact":"Use Harness Engineering for Self-Improvement to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from lilianweng.github.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"delegation","audience":"builder","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Lilian Weng","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"","publisher":"lilianweng.github.io","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0140","title":"Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime","url":"https://arxiv.org/abs/2607.11346","canonical_url":"https://arxiv.org/abs/2607.11346","annotation":"Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","key_contribution":"Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","impact":"Use Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.11346; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Chenglin Yu; Li Yin; Ying Yu; Qingxin Fan; RunyangRay Zhong; Hongxia Yang; Ming Li","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"9 pages, 3 figures, 5 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11346","date_added":"2026-07-15"},{"row_id":"ale-0141","title":"Mako: A Self-Evolving Agentic Operating System for Autonomous Web Exploitation","url":"https://arxiv.org/abs/2607.11288","canonical_url":"https://arxiv.org/abs/2607.11288","annotation":"Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","key_contribution":"Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","novelty":"Verification is promoted from a final check to a loop-control signal. Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","impact":"Use Mako: A Self-Evolving Agentic Operating System for Autonomous Web Exploitation to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.11288; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Praneeth Narisetty; Shiva Nagendra Babu Kore","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"13 pages, 10 figures, 8 tables","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11288","date_added":"2026-07-15"},{"row_id":"ale-0142","title":"How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study","url":"https://arxiv.org/abs/2607.10856","canonical_url":"https://arxiv.org/abs/2607.10856","annotation":"Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","key_contribution":"Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","novelty":"Verification is promoted from a final check to a loop-control signal. Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","impact":"Use How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.10856; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yunbo Lyu; David Williams; Jieke Shi; Zhensu Sun; Chao Peng; Zhou Yang; Federica Sarro; David Lo","publication_date":"2026-07-12","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.10856","date_added":"2026-07-15"},{"row_id":"ale-0143","title":"Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries","url":"https://arxiv.org/abs/2607.10113","canonical_url":"https://openreview.net/forum?id=cjU3YbcRr8","annotation":"Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","key_contribution":"Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","impact":"Use Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.10113; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yubo Li","publication_date":"2026","publication_year":"2026","publication_venue":"Transactions on Machine Learning Research (TMLR)","publisher":"OpenReview","doi":"","publication_note":"Accepted at Transactions on Machine Learning Research (TMLR); the linked arXiv record is the available paper version.","primary_category":"cs.AI","metadata_source":"Current arXiv acceptance note and OpenReview record","github_repo":"","github_stars":"","arxiv_id":"2607.10113","date_added":"2026-07-15"},{"row_id":"ale-0144","title":"SIA: Self Improving AI with Harness & Weight Updates","url":"https://arxiv.org/abs/2605.27276","canonical_url":"https://arxiv.org/abs/2605.27276","annotation":"Co-evolves a task agent's harness and model weights through a meta-agent, target agent, and feedback agent that evaluate outcomes and carry improvements across generations.","key_contribution":"Co-evolves a task agent's harness and model weights through a meta-agent, target agent, and feedback agent that evaluate outcomes and carry improvements across generations.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Co-evolves a task agent's harness and model weights through a meta-agent, target agent, and feedback agent that evaluate outcomes and carry improvements across generations.","impact":"Use SIA: Self Improving AI with Harness & Weight Updates to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2605.27276; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Prannay Hebbar; Yogendra Manawat; Samuel Verboomen; Alesia Ivanova; Selvam Palanimalai; Kunal Bhatia; Vignesh Baskaran","publication_date":"2026-05-26","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.27276","date_added":"2026-07-18"},{"row_id":"ale-0145","title":"Universal Transformers","url":"https://openreview.net/forum?id=HyzdRiR9Y7","canonical_url":"https://openreview.net/challenge?redirect=%2Fforum%3Fid%3DHyzdRiR9Y7","annotation":"Introduces recurrent depth for Transformers by repeatedly applying shared self-attention and transition blocks, with optional per-position adaptive halting; establishes the architectural foundation for later looped models.","key_contribution":"Introduces recurrent depth for Transformers by repeatedly applying shared self-attention and transition blocks, with optional per-position adaptive halting; establishes the architectural foundation for later looped models.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Introduces recurrent depth for Transformers by repeatedly applying shared self-attention and transition blocks, with optional per-position adaptive halting; establishes the architectural foundation for later looped models.","impact":"Use Universal Transformers to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Mostafa Dehghani; Stephan Gouws; Oriol Vinyals; Jakob Uszkoreit; Łukasz Kaiser","publication_date":"2019","publication_year":"2019","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"OpenReview","doi":"","publication_note":"Published at ICLR 2019; venue and authors verified from the official OpenReview record.","primary_category":"","metadata_source":"OpenReview","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0146","title":"Looped Transformers as Programmable Computers","url":"https://proceedings.mlr.press/v202/giannou23a.html","canonical_url":"https://proceedings.mlr.press/v202/giannou23a.html","annotation":"Constructs a constant-depth looped Transformer that advances an in-state program counter and executes reusable instructions, showing how iterative algorithms and in-context gradient descent can be represented through repeated shared computation.","key_contribution":"Constructs a constant-depth looped Transformer that advances an in-state program counter and executes reusable instructions, showing how iterative algorithms and in-context gradient descent can be represented through repeated shared computation.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Constructs a constant-depth looped Transformer that advances an in-state program counter and executes reusable instructions, showing how iterative algorithms and in-context gradient descent can be represented through repeated shared computation.","impact":"Use Looped Transformers as Programmable Computers to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;context;state","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Angeliki Giannou; Shashank Rajput; Jy-Yong Sohn; Kangwook Lee; Jason D. Lee; Dimitris Papailiopoulos","publication_date":"2023-07-03","publication_year":"2023","publication_venue":"International Conference on Machine Learning","publisher":"PMLR","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0147","title":"Looped Transformers are Better at Learning Learning Algorithms","url":"https://openreview.net/forum?id=HHbRxoDTxE","canonical_url":"https://openreview.net/challenge?redirect=%2Fforum%3Fid%3DHHbRxoDTxE","annotation":"Trains input-injected looped Transformers for in-context data fitting and shows that iterative shared computation can match standard Transformers on tested function classes with substantially fewer parameters.","key_contribution":"Trains input-injected looped Transformers for in-context data fitting and shows that iterative shared computation can match standard Transformers on tested function classes with substantially fewer parameters.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Trains input-injected looped Transformers for in-context data fitting and shows that iterative shared computation can match standard Transformers on tested function classes with substantially fewer parameters.","impact":"Use Looped Transformers are Better at Learning Learning Algorithms to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;context;verification","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Liu Yang; Kangwook Lee; Robert D. Nowak; Dimitris Papailiopoulos","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"OpenReview","doi":"","publication_note":"Published at ICLR 2024; venue and authors verified from the official OpenReview record.","primary_category":"","metadata_source":"OpenReview","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0148","title":"On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding","url":"https://proceedings.mlr.press/v267/xu25x.html","canonical_url":"https://proceedings.mlr.press/v267/xu25x.html","annotation":"Derives approximation rates for looped Transformers, identifies a loop-specific expressivity limit, and uses timestep-conditioned scaling to improve function approximation as recurrence increases.","key_contribution":"Derives approximation rates for looped Transformers, identifies a loop-specific expressivity limit, and uses timestep-conditioned scaling to improve function approximation as recurrence increases.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Derives approximation rates for looped Transformers, identifies a loop-specific expressivity limit, and uses timestep-conditioned scaling to improve function approximation as recurrence increases.","impact":"Use On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Kevin Xu; Issei Sato","publication_date":"2025-10-06","publication_year":"2025","publication_venue":"International Conference on Machine Learning","publisher":"PMLR","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0149","title":"Reasoning with Latent Thoughts: On the Power of Looped Transformers","url":"https://iclr.cc/virtual/2025/poster/28971","canonical_url":"https://iclr.cc/virtual/2025/poster/28971","annotation":"Connects effective recurrent depth to reasoning, proves that looped models can simulate multi-step chain-of-thought in latent space under the paper's construction, and studies the trade-off between reasoning and memorization.","key_contribution":"Connects effective recurrent depth to reasoning, proves that looped models can simulate multi-step chain-of-thought in latent space under the paper's construction, and studies the trade-off between reasoning and memorization.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Connects effective recurrent depth to reasoning, proves that looped models can simulate multi-step chain-of-thought in latent space under the paper's construction, and studies the trade-off between reasoning and memorization.","impact":"Use Reasoning with Latent Thoughts: On the Power of Looped Transformers to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Nikunj Saunshi; Nishanth Dikkala; Zhiyuan Li; Sanjiv Kumar; Sashank J. Reddi","publication_date":"2025","publication_year":"2025","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published at ICLR 2025; metadata verified from the official conference poster page.","primary_category":"","metadata_source":"ICLR proceedings","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0150","title":"Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach","url":"https://proceedings.neurips.cc/paper_files/paper/2025/hash/3b01972cf31e6fa0fe29e4b8b5c2a0a1-Abstract-Conference.html","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2025/hash/3b01972cf31e6fa0fe29e4b8b5c2a0a1-Abstract-Conference.html","annotation":"Presents Huginn, a 3.5B recurrent-depth language model trained on 800B tokens whose shared core can be unrolled further at inference, with gains concentrated on reasoning tasks and support for adaptive compute and KV-cache sharing.","key_contribution":"Presents Huginn, a 3.5B recurrent-depth language model trained on 800B tokens whose shared core can be unrolled further at inference, with gains concentrated on reasoning tasks and support for adaptive compute and KV-cache sharing.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Presents Huginn, a 3.5B recurrent-depth language model trained on 800B tokens whose shared core can be unrolled further at inference, with gains concentrated on reasoning tasks and support for adaptive compute and KV-cache sharing.","impact":"Use Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;verification;budget","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jonas Geiping; Sean McLeish; Neel Jain; John Kirchenbauer; Siddharth Singh; Brian Bartoldson; Bhavya Kailkhura; Abhinav Bhatele; Tom Goldstein","publication_date":"2025","publication_year":"2025","publication_venue":"Advances in Neural Information Processing Systems 38 (NeurIPS 2025)","publisher":"Neural Information Processing Systems Foundation","doi":"","publication_note":"Published at NeurIPS 2025; the proceedings page's later web timestamp is not used as the conference year.","primary_category":"","metadata_source":"NeurIPS proceedings","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0151","title":"Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation","url":"https://papers.nips.cc/paper_files/paper/2025/hash/8b08bbf8b420faa6eeb4020720582ec7-Abstract-Conference.html","canonical_url":"https://papers.nips.cc/paper_files/paper/2025/hash/8b08bbf8b420faa6eeb4020720582ec7-Abstract-Conference.html","annotation":"Combines shared recursive layers with token-level routers so difficult tokens receive more depth while attention and KV caching are restricted to active tokens.","key_contribution":"Combines shared recursive layers with token-level routers so difficult tokens receive more depth while attention and KV caching are restricted to active tokens.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Combines shared recursive layers with token-level routers so difficult tokens receive more depth while attention and KV caching are restricted to active tokens.","impact":"Use Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;budget","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Sangmin Bae; Yujin Kim; Reza Bayat; Sungnyun Kim; Jiyoun Ha; Tal Schuster; Adam Fisch; Hrayr Harutyunyan; Ziwei Ji; Aaron Courville; Se-Young Yun","publication_date":"2025","publication_year":"2025","publication_venue":"Advances in Neural Information Processing Systems 38 (NeurIPS 2025)","publisher":"Neural Information Processing Systems Foundation","doi":"","publication_note":"Published at NeurIPS 2025; the proceedings page's later web timestamp is not used as the conference year.","primary_category":"","metadata_source":"NeurIPS proceedings","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0152","title":"Scaling Latent Reasoning via Looped Language Models","url":"https://arxiv.org/abs/2510.25741","canonical_url":"https://arxiv.org/abs/2510.25741","annotation":"Introduces the Ouro family of pretrained LoopLMs, combining latent iteration, learned depth allocation, and large-scale pretraining to study recurrent depth as a scaling axis distinct from parameter count and generated reasoning tokens.","key_contribution":"Introduces the Ouro family of pretrained LoopLMs, combining latent iteration, learned depth allocation, and large-scale pretraining to study recurrent depth as a scaling axis distinct from parameter count and generated reasoning tokens.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Introduces the Ouro family of pretrained LoopLMs, combining latent iteration, learned depth allocation, and large-scale pretraining to study recurrent depth as a scaling axis distinct from parameter count and generated reasoning tokens.","impact":"Use Scaling Latent Reasoning via Looped Language Models to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source arXiv:2510.25741; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;budget","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Rui-Jie Zhu; Zixuan Wang; Kai Hua; Tianyu Zhang; Ziniu Li; Haoran Que; Boyi Wei; Zixin Wen; Fan Yin; He Xing; Lu Li; Jiajun Shi; Kaijing Ma; Shanda Li; Taylor Kergan; Andrew Smith; Xingwei Qu; Mude Hui; Bohong Wu; Qiyang Min; Hongzhi Huang; Xun Zhou; Wei Ye; Jiaheng Liu; Jian Yang; Yunfeng Shi; Chenghua Lin; Enduo Zhao; Tianle Cai; Ge Zhang; Wenhao Huang; Yoshua Bengio; Jason Eshraghian","publication_date":"2025-10-29","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2510.25741","date_added":"2026-07-18"},{"row_id":"ale-0153","title":"LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation","url":"https://iclr.cc/virtual/2026/poster/10009450","canonical_url":"https://iclr.cc/virtual/2026/poster/10009450","annotation":"Trains variable-length latent trajectories with time and step-size conditioning plus shortcut consistency, allowing one model to trade compute for quality across inference budgets without retraining.","key_contribution":"Trains variable-length latent trajectories with time and step-size conditioning plus shortcut consistency, allowing one model to trade compute for quality across inference budgets without retraining.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Trains variable-length latent trajectories with time and step-size conditioning plus shortcut consistency, allowing one model to trade compute for quality across inference budgets without retraining.","impact":"Use LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;budget","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Ahmadreza Jeddi; Marco Ciccone; Babak Taati","publication_date":"2026","publication_year":"2026","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published at ICLR 2026; metadata verified from the official conference poster page.","primary_category":"","metadata_source":"ICLR proceedings","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0154","title":"MoDr: Mixture-of-Depth-Recurrent Transformers for Test-Time Reasoning","url":"https://iclr.cc/virtual/2026/poster/10011117","canonical_url":"https://iclr.cc/virtual/2026/poster/10011117","annotation":"Replaces a single recurrent reasoning path with dynamically routed LoRA branches, adding solution-space exploration and load-balanced routing to the Huginn-style depth-recurrent backbone.","key_contribution":"Replaces a single recurrent reasoning path with dynamically routed LoRA branches, adding solution-space exploration and load-balanced routing to the Huginn-style depth-recurrent backbone.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Replaces a single recurrent reasoning path with dynamically routed LoRA branches, adding solution-space exploration and load-balanced routing to the Huginn-style depth-recurrent backbone.","impact":"Use MoDr: Mixture-of-Depth-Recurrent Transformers for Test-Time Reasoning to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;verification","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xiaojing Zhang; Haifeng Wu; Gang He; Jiyang Shen; Bochen Lyu; Zhanxing Zhu","publication_date":"2026","publication_year":"2026","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published at ICLR 2026; metadata verified from the official conference poster page.","primary_category":"","metadata_source":"ICLR proceedings","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0155","title":"ChainGPT: Dual-Reasoning Model with Recurrent Depth and Multi-Rank State Updates","url":"https://iclr.cc/virtual/2026/poster/10007767","canonical_url":"https://iclr.cc/virtual/2026/poster/10007767","annotation":"Combines within-layer multi-substep state updates, state-guided sparse attention, across-layer recurrence, and adaptive stopping to increase latent reasoning depth without extending visible chain-of-thought.","key_contribution":"Combines within-layer multi-substep state updates, state-guided sparse attention, across-layer recurrence, and adaptive stopping to increase latent reasoning depth without extending visible chain-of-thought.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Combines within-layer multi-substep state updates, state-guided sparse attention, across-layer recurrence, and adaptive stopping to increase latent reasoning depth without extending visible chain-of-thought.","impact":"Use ChainGPT: Dual-Reasoning Model with Recurrent Depth and Multi-Rank State Updates to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;state;exit","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yunao Zheng; Xiaojie Wang; Lei Ren; Chen Wei","publication_date":"2026","publication_year":"2026","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published at ICLR 2026; metadata verified from the official conference poster page.","primary_category":"","metadata_source":"ICLR proceedings","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0156","title":"Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models","url":"https://openreview.net/forum?id=eQaJSRZiGn","canonical_url":"https://openreview.net/challenge?redirect=%2Fforum%3Fid%3DeQaJSRZiGn","annotation":"Learns when a token needs extra latent refinement, using a neural decider, depth-aware LoRA, and cross-iteration attention to avoid always paying for or being degraded by additional loops.","key_contribution":"Learns when a token needs extra latent refinement, using a neural decider, depth-aware LoRA, and cross-iteration attention to avoid always paying for or being degraded by additional loops.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Learns when a token needs extra latent refinement, using a neural decider, depth-aware LoRA, and cross-iteration attention to avoid always paying for or being degraded by additional loops.","impact":"Use Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;budget","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Tianyu Fu; Yichen You; Zekai Chen; Guohao Dai; Huazhong Yang; Yu Wang","publication_date":"2026","publication_year":"2026","publication_venue":"International Conference on Machine Learning (ICML)","publisher":"OpenReview","doi":"","publication_note":"Published at ICML 2026; venue and authors verified from the official OpenReview record.","primary_category":"","metadata_source":"OpenReview","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0157","title":"Parcae: Scaling Laws For Stable Looped Language Models","url":"https://openreview.net/forum?id=ri0LAMdhd9","canonical_url":"https://openreview.net/challenge?redirect=%2Fforum%3Fid%3Dri0LAMdhd9","annotation":"Treats the recurrent block as a dynamical system, constrains its stability, and studies how training and test-time recurrence trade parameters for additional computation.","key_contribution":"Treats the recurrent block as a dynamical system, constrains its stability, and studies how training and test-time recurrence trade parameters for additional computation.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Treats the recurrent block as a dynamical system, constrains its stability, and studies how training and test-time recurrence trade parameters for additional computation.","impact":"Use Parcae: Scaling Laws For Stable Looped Language Models to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;verification","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Hayden Prairie; Zachary Novack; Taylor Berg-Kirkpatrick; Daniel Y. Fu","publication_date":"2026","publication_year":"2026","publication_venue":"Learning to Iterate Workshop at ICLR 2026","publisher":"OpenReview","doi":"","publication_note":"Workshop paper at the Learning to Iterate Workshop at ICLR 2026; not an ICLR main-conference paper.","primary_category":"","metadata_source":"OpenReview","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0158","title":"SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion","url":"https://arxiv.org/abs/2602.11698","canonical_url":"https://arxiv.org/abs/2602.11698","annotation":"Recurs over progressively compressed representations so repeated layers specialize across resolutions instead of recomputing every token at full resolution.","key_contribution":"Recurs over progressively compressed representations so repeated layers specialize across resolutions instead of recomputing every token at full resolution.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Recurs over progressively compressed representations so repeated layers specialize across resolutions instead of recomputing every token at full resolution.","impact":"Use SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source arXiv:2602.11698; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;budget","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Chengting Yu; Xiaobo Shu; Yadao Wang; Yizhen Zhang; Haoyi Wu; You Wu; Rujiao Long; Ziheng Chen; Yuchi Xu; Wenbo Su; Bo Zheng","publication_date":"2026-02-12","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2602.11698","date_added":"2026-07-18"},{"row_id":"ale-0159","title":"Training-Free Looped Transformers","url":"https://arxiv.org/abs/2605.23872","canonical_url":"https://arxiv.org/abs/2605.23872","annotation":"Retrofits recurrence onto frozen pretrained models by applying a damped mid-stack block as smaller refinement steps, testing when inference-time looping helps without fine-tuning.","key_contribution":"Retrofits recurrence onto frozen pretrained models by applying a damped mid-stack block as smaller refinement steps, testing when inference-time looping helps without fine-tuning.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Retrofits recurrence onto frozen pretrained models by applying a damped mid-stack block as smaller refinement steps, testing when inference-time looping helps without fine-tuning.","impact":"Use Training-Free Looped Transformers to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source arXiv:2605.23872; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;verification","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Lizhang Chen; Jonathan Li; Chen Liang; Ni Lao; Qiang Liu","publication_date":"2026-05-22","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.23872","date_added":"2026-07-18"},{"row_id":"ale-0160","title":"Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers","url":"https://arxiv.org/abs/2604.07822","canonical_url":"https://arxiv.org/abs/2604.07822","annotation":"Shows systematic generalization and depth extrapolation in controlled recurrent-depth reasoning tasks while documenting overthinking when recurrence exceeds the useful computation horizon.","key_contribution":"Shows systematic generalization and depth extrapolation in controlled recurrent-depth reasoning tasks while documenting overthinking when recurrence exceeds the useful computation horizon.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Shows systematic generalization and depth extrapolation in controlled recurrent-depth reasoning tasks while documenting overthinking when recurrence exceeds the useful computation horizon.","impact":"Use Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source arXiv:2604.07822; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Harsh Kohli; Srinivasan Parthasarathy; Huan Sun; Yuekun Yao","publication_date":"2026-04-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"19 pages, 18 figures. Under review","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.07822","date_added":"2026-07-18"},{"row_id":"ale-0161","title":"DeepLoop: Depth Scaling for Looped Transformers","url":"https://arxiv.org/abs/2607.13491","canonical_url":"https://arxiv.org/abs/2607.13491","annotation":"Derives residual-scaling rules that account for repeated parameter visits and tests them on GPT-style looped language models, addressing instability that nominal layer depth alone misses.","key_contribution":"Derives residual-scaling rules that account for repeated parameter visits and tests them on GPT-style looped language models, addressing instability that nominal layer depth alone misses.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Derives residual-scaling rules that account for repeated parameter visits and tests them on GPT-style looped language models, addressing instability that nominal layer depth alone misses.","impact":"Use DeepLoop: Depth Scaling for Looped Transformers to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source arXiv:2607.13491; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;verification","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Shuzhen Li; Yifan Zhang; Jiacheng Guo; Quanquan Gu; Mengdi Wang","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"25 pages","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13491","date_added":"2026-07-18"},{"row_id":"ale-0162","title":"How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models","url":"https://arxiv.org/abs/2604.21106","canonical_url":"https://arxiv.org/abs/2604.21106","annotation":"Separates recurrent passes from unique parameter depth and training compute to estimate when an additional loop is worth more than adding distinct layers.","key_contribution":"Separates recurrent passes from unique parameter depth and training compute to estimate when an additional loop is worth more than adding distinct layers.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Separates recurrent passes from unique parameter depth and training compute to estimate when an additional loop is worth more than adding distinct layers.","impact":"Use How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source arXiv:2604.21106; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Kristian Schwethelm; Daniel Rueckert; Georgios Kaissis","publication_date":"2026-04-22","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"v3: substantially refined framing + minor corrections v2: added case studies on truncated-BPTT and hyperconnections","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.21106","date_added":"2026-07-18"},{"row_id":"ale-0163","title":"LoopCoder: Scaling Code Intelligence via Looped Language Models","url":"https://aclanthology.org/2026.findings-acl.796/","canonical_url":"https://aclanthology.org/2026.findings-acl.796/","annotation":"Scales looped code models through dense-to-loop initialization, recurrent pretraining, and post-training; the Findings of ACL 2026 paper reports a 40B-active/80B-total model trained on more than 12T code and general tokens.","key_contribution":"Scales looped code models through dense-to-loop initialization, recurrent pretraining, and post-training; the Findings of ACL 2026 paper reports a 40B-active/80B-total model trained on more than 12T code and general tokens.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Scales looped code models through dense-to-loop initialization, recurrent pretraining, and post-training; the Findings of ACL 2026 paper reports a 40B-active/80B-total model trained on more than 12T code and general tokens.","impact":"Use LoopCoder: Scaling Code Intelligence via Looped Language Models to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;budget","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jian Yang; Wei Zhang; Shuyue Guo; Yizhi Li; Linzheng Chai; Zhengmao Ye; Shukai Liu; Yuyang Song; Jiajun Wu; Che Liu; Tianyu Zheng; Siwei Wu; Leo L; Xudong Ma; Chuan Hao; Ran Tao; Yan Xing; Jianzhou Wang; Mingjie Tang; Aishan Liu; Zhoujun Li; Xianglong Liu; Weifeng Lv; Bryan Dai","publication_date":"2026","publication_year":"2026","publication_venue":"Findings of the Association for Computational Linguistics: ACL 2026","publisher":"ACL Anthology","doi":"10.18653/v1/2026.findings-acl.796","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0164","title":"Looped World Models","url":"https://arxiv.org/abs/2606.18208","canonical_url":"https://arxiv.org/abs/2606.18208","annotation":"Introduces LoopWM, which repeatedly refines action-conditioned latent environment states with a parameter-shared Transformer core, stability constraints, and adaptive early exit for long-horizon simulation.","key_contribution":"Introduces LoopWM, which repeatedly refines action-conditioned latent environment states with a parameter-shared Transformer core, stability constraints, and adaptive early exit for long-horizon simulation.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Introduces LoopWM, which repeatedly refines action-conditioned latent environment states with a parameter-shared Transformer core, stability constraints, and adaptive early exit for long-horizon simulation.","impact":"Use Looped World Models to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source arXiv:2606.18208; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;exit","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Hongyuan Adam Lu; Z. L. Victor Wei; Qun Zhang; Jinrui Zeng; Bowen Cao; Lingwei Meng; Mocheng Li; Zezhong Wang; Haonan Yin; Naifu Xue; Minyu Chen; Cenyuan Zhang; Zefan Zhang; Hao Wei; Jiawei Zhou; Haoran Xu; Hao Yang; Ronglai Zuo; Tongda Xu; Yonghao Li; Jian Chen; Hebin Wang; Zeyu Gao; Yang Li; Wei Zhao; Qimin Zhong; Siqi Liu; Yumeng Zhang; Leyan Cui; Zhangyu Wang; Wai Lam","publication_date":"2026-06-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Technical Report","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.18208","date_added":"2026-07-18"},{"row_id":"ale-0165","title":"Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers","url":"https://arxiv.org/abs/2606.31779","canonical_url":"https://arxiv.org/abs/2606.31779","annotation":"Introduces LOTUS, which loops over parallel latent thought blocks with intermediate supervision and reports explicit-chain-of-thought-level quality at 3B scale with lower thought-phase latency.","key_contribution":"Introduces LOTUS, which loops over parallel latent thought blocks with intermediate supervision and reports explicit-chain-of-thought-level quality at 3B scale with lower thought-phase latency.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Introduces LOTUS, which loops over parallel latent thought blocks with intermediate supervision and reports explicit-chain-of-thought-level quality at 3B scale with lower thought-phase latency.","impact":"Use Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source arXiv:2606.31779; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Ying Fan; Anej Svete; Kangwook Lee","publication_date":"2026-06-30","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.31779","date_added":"2026-07-18"},{"row_id":"ale-0166","title":"Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification","url":"https://arxiv.org/abs/2605.16048","canonical_url":"https://arxiv.org/abs/2605.16048","annotation":"Extends tied-depth recurrence beyond Transformers by repeatedly applying a shared state-space block and reshaping inputs across iterations, testing which loop principles transfer across model families.","key_contribution":"Extends tied-depth recurrence beyond Transformers by repeatedly applying a shared state-space block and reshaping inputs across iterations, testing which loop principles transfer across model families.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Extends tied-depth recurrence beyond Transformers by repeatedly applying a shared state-space block and reshaping inputs across iterations, testing which loop principles transfer across model families.","impact":"Use Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source arXiv:2605.16048; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act;verification;state","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Mónika Farsang; Ramin Hasani; Daniela Rus; Radu Grosu","publication_date":"2026-05-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.16048","date_added":"2026-07-18"},{"row_id":"ale-0167","title":"Looped Diffusion Language Models","url":"https://arxiv.org/abs/2605.26106","canonical_url":"https://arxiv.org/abs/2605.26106","annotation":"Applies selective shared-layer recurrence to masked diffusion language models so effective depth can scale during training and inference without adding parameters.","key_contribution":"Applies selective shared-layer recurrence to masked diffusion language models so effective depth can scale during training and inference without adding parameters.","novelty":"Reuses learned computation inside one model inference rather than repeating a full agent run. Applies selective shared-layer recurrence to masked diffusion language models so effective depth can scale during training and inference without adding parameters.","impact":"Use Looped Diffusion Language Models to assess inner latent computation as a model capability inside a separately governed agent loop.","signal":"Research source arXiv:2605.26106; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand how recurrent model computation can power, but not replace, a governed agent loop.","section":"Model-Level Recurrence","section_slug":"model-level-recurrence","lifecycle_stages":"act","audience":"researcher;evaluator;model-builder;agent-builder","loop_layer":"model","scope_fit":"adjacent","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Sanghyun Lee; Chunsan Hong; Seungryong Kim; Jonghyun Lee; Jongho Park; Dongmin Park","publication_date":"2026-05-25","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"23 pages","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.26106","date_added":"2026-07-18"},{"row_id":"ale-0168","title":"Building Effective Agents","url":"https://www.anthropic.com/engineering/building-effective-agents","canonical_url":"https://www.anthropic.com/engineering/building-effective-agents","annotation":"Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","key_contribution":"Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","novelty":"Orchestration and control flow are made explicit and inspectable. Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","impact":"Use Building Effective Agents to turn a recurring-agent idea into an explicit loop contract.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Agent Workflow Patterns","section_slug":"agent-workflow-patterns","lifecycle_stages":"delegation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"technical-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0169","title":"Harness Engineering for Language Agents: The Harness Layer as Control, Agency, and Runtime","url":"https://www.preprints.org/manuscript/202603.1756","canonical_url":"https://www.preprints.org/manuscript/202603.1756","annotation":"Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","key_contribution":"Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","novelty":"Distills reusable agent-control patterns that are not tied to a single vendor implementation. 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Hassan; Hao Li; Dayi Lin; Bram Adams; Tse-Hsun Chen; Yutaro Kashiwa; Dong Qiu","publication_date":"2025-09-07","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2509.06216","date_added":""},{"row_id":"ale-0181","title":"The Art of Loop Engineering","url":"https://www.langchain.com/blog/the-art-of-loop-engineering","canonical_url":"https://www.langchain.com/blog/the-art-of-loop-engineering","annotation":"LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.","key_contribution":"LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.","novelty":"Verification is promoted from a final check to a loop-control signal. 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Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","key_contribution":"Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was \"a net wash\" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","novelty":"Verification is promoted from a final check to a loop-control signal. Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was \"a net wash\" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. 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Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions.","impact":"Use MemoHarness: Agent Harnesses That Learn from Experience to turn a recurring-agent idea into an explicit loop contract.","signal":"Research source arXiv:2607.14159; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Design","user_goal":"Specify a loop contract and operating pattern.","section":"Agent Workflow Patterns","section_slug":"agent-workflow-patterns","lifecycle_stages":"context","audience":"researcher;evaluator","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yue Huang; Wenjie Wang; Han Bao; Yuchen Ma; Xiaonan Luo; Yi Nian; Haomin Zhuang; Zheyuan Liu; Yue Zhao; Xiangliang Zhang","publication_date":"2026-07-14","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14159","date_added":"2026-07-17"},{"row_id":"ale-0189","title":"SWE-agent","url":"https://github.com/SWE-agent/SWE-agent","canonical_url":"https://github.com/SWE-agent/SWE-agent","annotation":"Agent-computer interface and autonomous software engineering agent for repository tasks.","key_contribution":"Agent-computer interface and autonomous software engineering agent for repository tasks.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Agent-computer interface and autonomous software engineering agent for repository tasks.","impact":"Use SWE-agent to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (19,840 stars; 2,168 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2024-04-02","publication_year":"2024","publication_venue":"SWE-agent/SWE-agent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"SWE-agent/SWE-agent","github_stars":"19840","arxiv_id":"","date_added":""},{"row_id":"ale-0190","title":"SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering","url":"https://arxiv.org/abs/2405.15793","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2024/hash/5a7c947568c1b1328ccc5230172e1e7c-Abstract-Conference.html","annotation":"Paper behind SWE-agent and its interface design.","key_contribution":"Paper behind SWE-agent and its interface design.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Paper behind SWE-agent and its interface design.","impact":"Use SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2405.15793; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"John Yang; Carlos E. Jimenez; Alexander Wettig; Kilian Lieret; Shunyu Yao; Karthik Narasimhan; Ofir Press","publication_date":"2024","publication_year":"2024","publication_venue":"Advances in Neural Information Processing Systems 37 (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"10.52202/079017-1601","publication_note":"Published in Advances in Neural Information Processing Systems 37 (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"NeurIPS proceedings and DOI records","github_repo":"","github_stars":"","arxiv_id":"2405.15793","date_added":""},{"row_id":"ale-0191","title":"mini-SWE-agent","url":"https://mini-swe-agent.com/latest/","canonical_url":"https://mini-swe-agent.com/latest/","annotation":"Minimal coding agent that is useful for understanding the core loop without a large framework.","key_contribution":"Minimal coding agent that is useful for understanding the core loop without a large framework.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Minimal coding agent that is useful for understanding the core loop without a large framework.","impact":"Use mini-SWE-agent to choose an implementation surface for repeatable agent work.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"implementation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"mini-swe-agent.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0192","title":"OpenHands","url":"https://github.com/All-Hands-AI/OpenHands","canonical_url":"https://github.com/OpenHands/OpenHands","annotation":"Open platform for AI software developers as generalist agents.","key_contribution":"Open platform for AI software developers as generalist agents.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Open platform for AI software developers as generalist agents.","impact":"Use OpenHands to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (81,153 stars; 10,375 forks; NOASSERTION license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2024-03-13","publication_year":"2024","publication_venue":"All-Hands-AI/OpenHands","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"All-Hands-AI/OpenHands","github_stars":"81153","arxiv_id":"","date_added":""},{"row_id":"ale-0193","title":"OpenHands: An Open Platform for AI Software Developers as Generalist Agents","url":"https://arxiv.org/abs/2407.16741","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2025/hash/a4b6ad6b48850c0c331d1259fc66a69c-Abstract-Conference.html","annotation":"Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","key_contribution":"Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","impact":"Use OpenHands: An Open Platform for AI Software Developers as Generalist Agents to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2407.16741; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xingyao Wang; Boxuan Li; Yufan Song; Frank F. Xu; Xiangru Tang; Mingchen Zhuge; Jiayi Pan; Yueqi Song; Bowen Li; Jaskirat Singh; Hoang H. Tran; Fuqiang Li; Ren Ma; Mingzhang Zheng; Bill Qian; Yanjun Shao; Niklas Muennighoff; Yizhe Zhang; Binyuan Hui; Junyang Lin; Robert Brennan; Hao Peng; Heng Ji; Graham Neubig","publication_date":"2025","publication_year":"2025","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2407.16741","date_added":""},{"row_id":"ale-0194","title":"Agentless","url":"https://github.com/OpenAutoCoder/Agentless","canonical_url":"https://github.com/OpenAutoCoder/Agentless","annotation":"Workflow-based approach for software issue resolution using localization, repair, and patch validation.","key_contribution":"Workflow-based approach for software issue resolution using localization, repair, and patch validation.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Workflow-based approach for software issue resolution using localization, repair, and patch validation.","impact":"Use Agentless to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (2,083 stars; 235 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"intake","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2024-06-30","publication_year":"2024","publication_venue":"OpenAutoCoder/Agentless","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"OpenAutoCoder/Agentless","github_stars":"2083","arxiv_id":"","date_added":""},{"row_id":"ale-0195","title":"Agentless: Demystifying LLM-based Software Engineering Agents","url":"https://arxiv.org/abs/2407.01489","canonical_url":"https://arxiv.org/abs/2407.01489","annotation":"Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.","key_contribution":"Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.","impact":"Use Agentless: Demystifying LLM-based Software Engineering Agents to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2407.01489; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Chunqiu Steven Xia; Yinlin Deng; Soren Dunn; Lingming Zhang","publication_date":"2024-07-01","publication_year":"2024","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2407.01489","date_added":""},{"row_id":"ale-0196","title":"AutoCodeRover","url":"https://github.com/AutoCodeRoverSG/auto-code-rover","canonical_url":"https://github.com/AutoCodeRoverSG/auto-code-rover","annotation":"Autonomous program improvement system for issue localization, patch generation, and validation.","key_contribution":"Autonomous program improvement system for issue localization, patch generation, and validation.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Autonomous program improvement system for issue localization, patch generation, and validation.","impact":"Use AutoCodeRover to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (3,096 stars; 334 forks; NOASSERTION license; updated 2026-07-14); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"intake","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2024-04-08","publication_year":"2024","publication_venue":"AutoCodeRoverSG/auto-code-rover","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"AutoCodeRoverSG/auto-code-rover","github_stars":"3096","arxiv_id":"","date_added":""},{"row_id":"ale-0197","title":"AutoCodeRover: Autonomous Program Improvement","url":"https://arxiv.org/abs/2404.05427","canonical_url":"https://doi.org/10.1145/3650212.3680384","annotation":"Paper on autonomous code repair loops over real repositories.","key_contribution":"Paper on autonomous code repair loops over real repositories.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Paper on autonomous code repair loops over real repositories.","impact":"Use AutoCodeRover: Autonomous Program Improvement to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2404.05427; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yuntong Zhang; Haifeng Ruan; Zhiyu Fan; Abhik Roychoudhury","publication_date":"2024-09-11","publication_year":"2024","publication_venue":"Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)","publisher":"Association for Computing Machinery","doi":"10.1145/3650212.3680384","publication_note":"Published in Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ACM DOI record","github_repo":"","github_stars":"","arxiv_id":"2404.05427","date_added":""},{"row_id":"ale-0198","title":"SWE-bench reading list","url":"https://github.com/SWE-bench/reading-list","canonical_url":"https://github.com/SWE-bench/reading-list","annotation":"Maintained map of software engineering agent systems and related papers.","key_contribution":"Maintained map of software engineering agent systems and related papers.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Maintained map of software engineering agent systems and related papers.","impact":"Use SWE-bench reading list to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (15 stars; 4 forks; updated 2026-06-30); popularity is context, not proof of reliability.","resource_type":"List","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"curated-index","evidence_tier":"C","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-06-26","publication_year":"2025","publication_venue":"SWE-bench/reading-list","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"SWE-bench/reading-list","github_stars":"15","arxiv_id":"","date_added":""},{"row_id":"ale-0199","title":"TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code","url":"https://arxiv.org/abs/2602.06875","canonical_url":"https://conf.researchr.org/details/icse-2026/icse-2026-research-track/145/TraceCoder-A-Trace-Driven-Multi-Agent-Framework-for-Automated-Debugging-of-LLM-Gener","annotation":"ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","key_contribution":"ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","impact":"Use TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2602.06875; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;state","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jiangping Huang; Wenguang Ye; Weisong Sun; Jian Zhang; Mingyue Zhang; Yang Liu","publication_date":"2026-04-12","publication_year":"2026","publication_venue":"Proceedings of the 48th IEEE/ACM International Conference on Software Engineering (ICSE)","publisher":"Association for Computing Machinery","doi":"10.1145/3744916.3773187","publication_note":"Published in Proceedings of the 48th IEEE/ACM International Conference on Software Engineering (ICSE); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ICSE program and camera-ready records","github_repo":"","github_stars":"","arxiv_id":"2602.06875","date_added":""},{"row_id":"ale-0200","title":"The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase","url":"https://arxiv.org/abs/2603.25697","canonical_url":"https://arxiv.org/abs/2603.25697","annotation":"Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","key_contribution":"Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","impact":"Use The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2603.25697; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yannick Roy","publication_date":"2026-03-26","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.25697","date_added":""},{"row_id":"ale-0201","title":"Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures","url":"https://arxiv.org/abs/2604.03515","canonical_url":"https://arxiv.org/abs/2604.03515","annotation":"Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","key_contribution":"Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","novelty":"State persistence is explicit enough for repeated runs and handoff. Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","impact":"Use Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2604.03515; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;verification;state;budget","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Benjamin Rombaut","publication_date":"2026-04-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.03515","date_added":""},{"row_id":"ale-0202","title":"A Self-Improving Coding Agent","url":"https://arxiv.org/abs/2504.15228","canonical_url":"https://arxiv.org/abs/2504.15228","annotation":"An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","key_contribution":"An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","novelty":"Verification is promoted from a final check to a loop-control signal. An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","impact":"Use A Self-Improving Coding Agent to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2504.15228; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Maxime Robeyns; Martin Szummer; Laurence Aitchison","publication_date":"2025-04-21","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Submitted as a preprint to NeurIPS 2025","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2504.15228","date_added":""},{"row_id":"ale-0203","title":"Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality","url":"https://arxiv.org/abs/2607.03691","canonical_url":"https://arxiv.org/abs/2607.03691","annotation":"Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","key_contribution":"Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","impact":"Use Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.03691; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;context","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Oussama Ben Sghaier; Hao Li; Bram Adams; Ahmed E. Hassan","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.03691","date_added":""},{"row_id":"ale-0204","title":"ToFu: A White-Box, Token-Efficient Agent Harness for Researchers","url":"https://arxiv.org/abs/2607.11423","canonical_url":"https://arxiv.org/abs/2607.11423","annotation":"MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","key_contribution":"MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","novelty":"Orchestration and control flow are made explicit and inspectable. MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","impact":"Use ToFu: A White-Box, Token-Efficient Agent Harness for Researchers to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.11423; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;budget","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Junhao Ruan; Yuan Ge; Bei Li; Yongjing Yin; Yuchun Fan; Xin Chen; Jingang Wang; Chenglong Wang; Jingbo Zhu; Tong Xiao","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11423","date_added":"2026-07-15"},{"row_id":"ale-0205","title":"When Does Restricting a Coding Agent to execute_code Help?","url":"https://arxiv.org/abs/2607.10569","canonical_url":"https://arxiv.org/abs/2607.10569","annotation":"Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","key_contribution":"Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","impact":"Use When Does Restricting a Coding Agent to execute_code Help? to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.10569; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Hong Yang; Qi Yu; Travis Desell","publication_date":"2026","publication_year":"2026","publication_venue":"KDD Workshop on Agentic Software Engineering (SE 3.0)","publisher":"ACM SIGKDD","doi":"","publication_note":"Accepted at KDD Workshop on Agentic Software Engineering (SE 3.0); the linked arXiv record is the available paper version.","primary_category":"cs.SE","metadata_source":"Current arXiv acceptance note and official non-archival workshop page","github_repo":"","github_stars":"","arxiv_id":"2607.10569","date_added":"2026-07-15"},{"row_id":"ale-0206","title":"Ralph","url":"https://ghuntley.com/ralph/","canonical_url":"https://ghuntley.com/ralph/","annotation":"Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.","key_contribution":"Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.","novelty":"Persistent memory is treated as an external runtime artifact. Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.","impact":"Use Ralph to choose an implementation surface for repeatable agent work.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"operational-pattern","evidence_tier":"B","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-07-14","publication_year":"2025","publication_venue":"","publisher":"Geoffrey Huntley","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0207","title":"everything is a ralph loop","url":"https://ghuntley.com/loop/","canonical_url":"https://ghuntley.com/loop/","annotation":"Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","key_contribution":"Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","impact":"Use everything is a ralph loop to choose an implementation surface for repeatable agent work.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context;verification","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"operational-pattern","evidence_tier":"B","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-17","publication_year":"2026","publication_venue":"","publisher":"Geoffrey Huntley","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0208","title":"how-to-ralph-wiggum","url":"https://github.com/ghuntley/how-to-ralph-wiggum","canonical_url":"https://github.com/ghuntley/how-to-ralph-wiggum","annotation":"Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","key_contribution":"Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","impact":"Use how-to-ralph-wiggum to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,720 stars; 146 forks; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-10","publication_year":"2026","publication_venue":"ghuntley/how-to-ralph-wiggum","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ghuntley/how-to-ralph-wiggum","github_stars":"1720","arxiv_id":"","date_added":""},{"row_id":"ale-0209","title":"A Brief History of Ralph","url":"https://www.humanlayer.dev/blog/brief-history-of-ralph","canonical_url":"https://www.humanlayer.dev/blog/brief-history-of-ralph","annotation":"Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.","key_contribution":"Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.","impact":"Use A Brief History of Ralph to choose an implementation surface for repeatable agent work.","signal":"Contextual source from www.humanlayer.dev; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026","publication_year":"2026","publication_venue":"","publisher":"humanlayer.dev","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0210","title":"Ralph Copilot","url":"https://github.com/giocaizzi/ralph-copilot/tree/e5b2813cc876c73a8c9d3398c0115da0d15f63cf","canonical_url":"https://github.com/giocaizzi/ralph-copilot/tree/e5b2813cc876c73a8c9d3398c0115da0d15f63cf","annotation":"Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","key_contribution":"Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","novelty":"Persistent memory is treated as an external runtime artifact. Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","impact":"Use Ralph Copilot to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (138 stars; 16 forks; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Pattern","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"operational-pattern","evidence_tier":"B","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-30","publication_year":"2026","publication_venue":"giocaizzi/ralph-copilot","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"giocaizzi/ralph-copilot","github_stars":"138","arxiv_id":"","date_added":""},{"row_id":"ale-0211","title":"Ralph (snarktank)","url":"https://github.com/snarktank/ralph","canonical_url":"https://github.com/snarktank/ralph","annotation":"Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","key_contribution":"Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","novelty":"State persistence is explicit enough for repeated runs and handoff. Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","impact":"Use Ralph (snarktank) to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (21,117 stars; 2,045 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-07","publication_year":"2026","publication_venue":"snarktank/ralph","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"snarktank/ralph","github_stars":"21117","arxiv_id":"","date_added":""},{"row_id":"ale-0212","title":"ralph-claude-code","url":"https://github.com/frankbria/ralph-claude-code","canonical_url":"https://github.com/frankbria/ralph-claude-code","annotation":"Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","key_contribution":"Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","impact":"Use ralph-claude-code to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (9,546 stars; 729 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"exit","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-08-27","publication_year":"2025","publication_venue":"frankbria/ralph-claude-code","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"frankbria/ralph-claude-code","github_stars":"9546","arxiv_id":"","date_added":""},{"row_id":"ale-0213","title":"ralph-orchestrator","url":"https://github.com/mikeyobrien/ralph-orchestrator","canonical_url":"https://github.com/mikeyobrien/ralph-orchestrator","annotation":"Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","key_contribution":"Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","novelty":"Orchestration and control flow are made explicit and inspectable. Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","impact":"Use ralph-orchestrator to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (3,031 stars; 286 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;escalation;exit","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-09-07","publication_year":"2025","publication_venue":"mikeyobrien/ralph-orchestrator","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"mikeyobrien/ralph-orchestrator","github_stars":"3031","arxiv_id":"","date_added":""},{"row_id":"ale-0214","title":"ralphex","url":"https://github.com/umputun/ralphex","canonical_url":"https://github.com/umputun/ralphex","annotation":"Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","key_contribution":"Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","impact":"Use ralphex to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,384 stars; 113 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-19","publication_year":"2026","publication_venue":"umputun/ralphex","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"umputun/ralphex","github_stars":"1384","arxiv_id":"","date_added":""},{"row_id":"ale-0215","title":"ralph (iannuttall)","url":"https://github.com/iannuttall/ralph","canonical_url":"https://github.com/iannuttall/ralph","annotation":"File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","key_contribution":"File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","novelty":"Persistent memory is treated as an external runtime artifact. File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","impact":"Use ralph (iannuttall) to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (932 stars; 91 forks; updated 2026-07-11); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-12","publication_year":"2026","publication_venue":"iannuttall/ralph","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"iannuttall/ralph","github_stars":"932","arxiv_id":"","date_added":""},{"row_id":"ale-0216","title":"ralph-loop-agent","url":"https://github.com/vercel-labs/ralph-loop-agent","canonical_url":"https://github.com/vercel-labs/ralph-loop-agent","annotation":"Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","key_contribution":"Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","novelty":"Verification is promoted from a final check to a loop-control signal. Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","impact":"Use ralph-loop-agent to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (821 stars; 86 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;budget;exit","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-03","publication_year":"2026","publication_venue":"vercel-labs/ralph-loop-agent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"vercel-labs/ralph-loop-agent","github_stars":"821","arxiv_id":"","date_added":""},{"row_id":"ale-0217","title":"Open Ralph Wiggum","url":"https://github.com/Th0rgal/open-ralph-wiggum","canonical_url":"https://github.com/Th0rgal/open-ralph-wiggum","annotation":"Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","key_contribution":"Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","impact":"Use Open Ralph Wiggum to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,843 stars; 142 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-06","publication_year":"2026","publication_venue":"Th0rgal/open-ralph-wiggum","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Th0rgal/open-ralph-wiggum","github_stars":"1843","arxiv_id":"","date_added":""},{"row_id":"ale-0218","title":"Compound Engineering","url":"https://every.to/guides/compound-engineering","canonical_url":"https://every.to/guides/compound-engineering","annotation":"Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","key_contribution":"Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","novelty":"Persistent memory is treated as an external runtime artifact. Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","impact":"Use Compound Engineering to choose an implementation surface for repeatable agent work.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"operational-pattern","evidence_tier":"B","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"every.to","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0219","title":"Gas Town","url":"https://github.com/steveyegge/gastown","canonical_url":"https://github.com/gastownhall/gastown","annotation":"Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","key_contribution":"Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","impact":"Use Gas Town to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (17,084 stars; 1,572 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"intake;delegation","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-12-16","publication_year":"2025","publication_venue":"steveyegge/gastown","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"steveyegge/gastown","github_stars":"17084","arxiv_id":"","date_added":""},{"row_id":"ale-0220","title":"Amp","url":"https://ampcode.com/","canonical_url":"https://ampcode.com/","annotation":"Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","key_contribution":"Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","impact":"Use Amp to choose an implementation surface for repeatable agent work.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;context;delegation","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"implementation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"ampcode.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0221","title":"karl","url":"https://github.com/kayoslab/karl","canonical_url":"https://github.com/kayoslab/karl","annotation":"Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","key_contribution":"Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","impact":"Use karl to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (0 stars; 0 forks; MIT license; updated 2026-04-08); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;budget","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-10","publication_year":"2026","publication_venue":"kayoslab/karl","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"kayoslab/karl","github_stars":"0","arxiv_id":"","date_added":""},{"row_id":"ale-0222","title":"joelclaw agent-loop skill","url":"https://github.com/joelhooks/joelclaw/blob/main/skills/agent-loop/SKILL.md","canonical_url":"https://github.com/joelhooks/joelclaw/blob/main/skills/agent-loop/SKILL.md","annotation":"Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.","key_contribution":"Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.","impact":"Use joelclaw agent-loop skill to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (61 stars; 3 forks; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Pattern","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"operational-pattern","evidence_tier":"B","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-02-14","publication_year":"2026","publication_venue":"joelhooks/joelclaw","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"joelhooks/joelclaw","github_stars":"61","arxiv_id":"","date_added":""},{"row_id":"ale-0223","title":"ARIS (Auto-Research-In-Sleep)","url":"https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep","canonical_url":"https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep","annotation":"Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","key_contribution":"Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","novelty":"Verification is promoted from a final check to a loop-control signal. Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","impact":"Use ARIS (Auto-Research-In-Sleep) to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (13,538 stars; 1,220 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"intake;verification","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-10","publication_year":"2026","publication_venue":"wanshuiyin/Auto-claude-code-research-in-sleep","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"wanshuiyin/Auto-claude-code-research-in-sleep","github_stars":"13538","arxiv_id":"","date_added":""},{"row_id":"ale-0224","title":"AutoAgent","url":"https://github.com/kevinrgu/autoagent","canonical_url":"https://github.com/kevinrgu/autoagent","annotation":"Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","key_contribution":"Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","novelty":"The work turns loop quality into a measurable task or score. Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","impact":"Use AutoAgent to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (4,544 stars; 498 forks; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-02","publication_year":"2026","publication_venue":"kevinrgu/autoagent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"kevinrgu/autoagent","github_stars":"4544","arxiv_id":"","date_added":""},{"row_id":"ale-0225","title":"zeroshot","url":"https://github.com/the-open-engine/zeroshot","canonical_url":"https://github.com/the-open-engine/zeroshot","annotation":"CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","key_contribution":"CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","novelty":"Verification is promoted from a final check to a loop-control signal. CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","impact":"Use zeroshot to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,647 stars; 141 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-12-25","publication_year":"2025","publication_venue":"the-open-engine/zeroshot","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"the-open-engine/zeroshot","github_stars":"1647","arxiv_id":"","date_added":""},{"row_id":"ale-0226","title":"Loki Mode","url":"https://github.com/asklokesh/loki-mode","canonical_url":"https://github.com/asklokesh/loki-mode","annotation":"Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","key_contribution":"Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","impact":"Use Loki Mode to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,022 stars; 199 forks; NOASSERTION license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification;state;exit","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-12-26","publication_year":"2025","publication_venue":"asklokesh/loki-mode","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"asklokesh/loki-mode","github_stars":"1022","arxiv_id":"","date_added":""},{"row_id":"ale-0227","title":"Looper","url":"https://github.com/ksimback/looper","canonical_url":"https://github.com/ksimback/looper","annotation":"Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","key_contribution":"Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","novelty":"Verification is promoted from a final check to a loop-control signal. Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","impact":"Use Looper to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (685 stars; 62 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"objective;verification;budget","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-18","publication_year":"2026","publication_venue":"ksimback/looper","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ksimback/looper","github_stars":"685","arxiv_id":"","date_added":""},{"row_id":"ale-0228","title":"Agent Apprenticeship","url":"https://github.com/Forsy-AI/agent-apprenticeship","canonical_url":"https://github.com/ray-r-ren/agent-apprenticeship","annotation":"Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","key_contribution":"Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","novelty":"Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","impact":"Use Agent Apprenticeship to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,317 stars; 56 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-19","publication_year":"2026","publication_venue":"Forsy-AI/agent-apprenticeship","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Forsy-AI/agent-apprenticeship","github_stars":"1317","arxiv_id":"","date_added":""},{"row_id":"ale-0229","title":"Scholar Loop","url":"https://github.com/renee-jia/scholar-loop","canonical_url":"https://github.com/renee-jia/scholar-loop","annotation":"Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","key_contribution":"Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","impact":"Use Scholar Loop to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (461 stars; 36 forks; MIT license; updated 2026-07-08); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-15","publication_year":"2026","publication_venue":"renee-jia/scholar-loop","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"renee-jia/scholar-loop","github_stars":"461","arxiv_id":"","date_added":""},{"row_id":"ale-0230","title":"loop-engineering (Cobus Greyling)","url":"https://github.com/cobusgreyling/loop-engineering","canonical_url":"https://github.com/cobusgreyling/loop-engineering","annotation":"Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's \"Loop Ready\" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","key_contribution":"Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's \"Loop Ready\" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's \"Loop Ready\" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","impact":"Use loop-engineering (Cobus Greyling) to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (8,353 stars; 1,107 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;workspace;context;state;budget","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-09","publication_year":"2026","publication_venue":"cobusgreyling/loop-engineering","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"cobusgreyling/loop-engineering","github_stars":"8353","arxiv_id":"","date_added":""},{"row_id":"ale-0231","title":"AutoCVE","url":"https://github.com/larlarua/AutoCVE","canonical_url":"https://github.com/larlarua/AutoCVE","annotation":"Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","key_contribution":"Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","novelty":"Verification is promoted from a final check to a loop-control signal. Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","impact":"Use AutoCVE to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,302 stars; 89 forks; AGPL-3.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"intake;delegation;verification;exit","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-15","publication_year":"2026","publication_venue":"larlarua/AutoCVE","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"larlarua/AutoCVE","github_stars":"1302","arxiv_id":"","date_added":""},{"row_id":"ale-0232","title":"LoongFlow (Baidu)","url":"https://github.com/baidu-baige/LoongFlow","canonical_url":"https://github.com/baidu-baige/LoongFlow","annotation":"Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","key_contribution":"Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","novelty":"Persistent memory is treated as an external runtime artifact. Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","impact":"Use LoongFlow (Baidu) to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (452 stars; 52 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-12-31","publication_year":"2025","publication_venue":"baidu-baige/LoongFlow","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"baidu-baige/LoongFlow","github_stars":"452","arxiv_id":"","date_added":""},{"row_id":"ale-0233","title":"cc10x","url":"https://github.com/romiluz13/cc10x","canonical_url":"https://github.com/romiluz13/cc10x","annotation":"Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","key_contribution":"Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","impact":"Use cc10x to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (153 stars; 25 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context;verification","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-10-22","publication_year":"2025","publication_venue":"romiluz13/cc10x","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"romiluz13/cc10x","github_stars":"153","arxiv_id":"","date_added":""},{"row_id":"ale-0234","title":"RigorLoop","url":"https://github.com/ronikobrosly/RigorLoop","canonical_url":"https://github.com/ronikobrosly/RigorLoop","annotation":"Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.","key_contribution":"Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.","impact":"Use RigorLoop to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (134 stars; 1 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"ronikobrosly/RigorLoop","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ronikobrosly/RigorLoop","github_stars":"134","arxiv_id":"","date_added":""},{"row_id":"ale-0235","title":"Open-Inspect","url":"https://github.com/ColeMurray/background-agents","canonical_url":"https://github.com/ColeMurray/background-agents","annotation":"Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","key_contribution":"Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","novelty":"Execution isolation and permission boundaries are part of the design. Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","impact":"Use Open-Inspect to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (2,495 stars; 356 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-25","publication_year":"2026","publication_venue":"ColeMurray/background-agents","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ColeMurray/background-agents","github_stars":"2495","arxiv_id":"","date_added":""},{"row_id":"ale-0236","title":"T3MP3ST","url":"https://github.com/elder-plinius/T3MP3ST","canonical_url":"https://github.com/elder-plinius/T3MP3ST","annotation":"Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","key_contribution":"Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","impact":"Use T3MP3ST to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (4,912 stars; 1,027 forks; AGPL-3.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"elder-plinius/T3MP3ST","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"elder-plinius/T3MP3ST","github_stars":"4912","arxiv_id":"","date_added":""},{"row_id":"ale-0237","title":"Loom","url":"https://github.com/valkor-ai/loom","canonical_url":"https://github.com/valkor-ai/loom","annotation":"Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","key_contribution":"Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","impact":"Use Loom to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (587 stars; 63 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-09","publication_year":"2026","publication_venue":"valkor-ai/loom","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"valkor-ai/loom","github_stars":"587","arxiv_id":"","date_added":""},{"row_id":"ale-0238","title":"Inferoa","url":"https://github.com/agentic-in/inferoa","canonical_url":"https://github.com/agentic-in/inferoa","annotation":"Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","key_contribution":"Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","impact":"Use Inferoa to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (486 stars; 84 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-08","publication_year":"2026","publication_venue":"agentic-in/inferoa","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"agentic-in/inferoa","github_stars":"486","arxiv_id":"","date_added":""},{"row_id":"ale-0239","title":"PlanWeave","url":"https://github.com/GaosCode/PlanWeave","canonical_url":"https://github.com/GaosCode/PlanWeave","annotation":"File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.","key_contribution":"File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.","impact":"Use PlanWeave to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (226 stars; 15 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-05-24","publication_year":"2026","publication_venue":"GaosCode/PlanWeave","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"GaosCode/PlanWeave","github_stars":"226","arxiv_id":"","date_added":""},{"row_id":"ale-0240","title":"loop.js","url":"https://github.com/loop-js/loop.js","canonical_url":"https://github.com/loop-js/loop.js","annotation":"TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","key_contribution":"TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","novelty":"Verification is promoted from a final check to a loop-control signal. TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","impact":"Use loop.js to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (126 stars; 1 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"objective;budget","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"loop-js/loop.js","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"loop-js/loop.js","github_stars":"126","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0241","title":"ai-trains-ai","url":"https://github.com/Danau5tin/ai-trains-ai","canonical_url":"https://github.com/Danau5tin/ai-trains-ai","annotation":"Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","key_contribution":"Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","impact":"Use ai-trains-ai to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (189 stars; 14 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"Danau5tin/ai-trains-ai","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Danau5tin/ai-trains-ai","github_stars":"189","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0242","title":"Factory 2.0: From Coding Agents to Software Factories","url":"https://factory.ai/news/software-factory","canonical_url":"https://factory.ai/news/software-factory","annotation":"Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","key_contribution":"Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","novelty":"Persistent memory is treated as an external runtime artifact. Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","impact":"Use Factory 2.0: From Coding Agents to Software Factories to choose an implementation surface for repeatable agent work.","signal":"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"objective;context;delegation;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Factory","publication_date":"2026-06-15","publication_year":"2026","publication_venue":"","publisher":"Factory","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0243","title":"Superpowers 6","url":"https://blog.fsck.com/2026/06/15/Superpowers-6/","canonical_url":"https://blog.fsck.com/2026/06/15/Superpowers-6/","annotation":"Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","key_contribution":"Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","impact":"Use Superpowers 6 to choose an implementation surface for repeatable agent work.","signal":"Contextual source from blog.fsck.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;verification;budget","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"Massively Parallel Procrastination","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0244","title":"Introducing Devin Security Swarm","url":"https://cognition.com/blog/introducing-devin-security-swarm","canonical_url":"https://cognition.com/blog/introducing-devin-security-swarm","annotation":"Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","key_contribution":"Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","novelty":"The trigger or cadence is explicit, making the workflow recurring rather than one-off. Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","impact":"Use Introducing Devin Security Swarm to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;intake;workspace;verification","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"","publisher":"cognition.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0245","title":"Towards Self-Driving Codebases","url":"https://cursor.com/blog/self-driving-codebases","canonical_url":"https://cursor.com/blog/self-driving-codebases","annotation":"Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","key_contribution":"Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","impact":"Use Towards Self-Driving Codebases to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Wilson Lin","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0246","title":"Factory: Incident Response Automation","url":"https://factory.ai/news/incident-response","canonical_url":"https://factory.ai/news/incident-response","annotation":"Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","key_contribution":"Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","novelty":"State persistence is explicit enough for repeated runs and handoff. Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","impact":"Use Factory: Incident Response Automation to choose an implementation surface for repeatable agent work.","signal":"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Factory","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"","publisher":"Factory","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0247","title":"A Week-Long Autonomous Voxel Manhattan Build","url":"https://x.com/mattshumer_/status/2075268746315268138","canonical_url":"https://x.com/mattshumer_/status/2075268746315268138","annotation":"Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.","key_contribution":"Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.","impact":"Use A Week-Long Autonomous Voxel Manhattan Build to choose an implementation surface for repeatable agent work.","signal":"Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"","publisher":"X (formerly Twitter)","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0248","title":"Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework","url":"https://arxiv.org/abs/2607.13091","canonical_url":"https://arxiv.org/abs/2607.13091","annotation":"Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","key_contribution":"Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","impact":"Use Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.13091; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"researcher;evaluator","loop_layer":"agent","scope_fit":"direct","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Aditya Aggarwal; Nahid Farhady Ghalaty","publication_date":"2026-06-22","publication_year":"2026","publication_venue":"32nd IEEE International Conference on Engineering Technology and Innovation (ICE/ITMC)","publisher":"IEEE","doi":"","publication_note":"Accepted at 32nd IEEE International Conference on Engineering Technology and Innovation (ICE/ITMC); the linked arXiv record is the available paper version.","primary_category":"cs.SE","metadata_source":"Current arXiv acceptance note and official conference page","github_repo":"","github_stars":"","arxiv_id":"2607.13091","date_added":"2026-07-17"},{"row_id":"ale-0249","title":"Webwright","url":"https://github.com/microsoft/Webwright","canonical_url":"https://github.com/microsoft/Webwright","annotation":"Treats workspace code, screenshots, and run artifacts as durable state while browsers remain disposable, producing a readable write-run-inspect-repair loop for long-horizon web tasks.","key_contribution":"Treats workspace code, screenshots, and run artifacts as durable state while browsers remain disposable, producing a readable write-run-inspect-repair loop for long-horizon web tasks.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Treats workspace code, screenshots, and run artifacts as durable state while browsers remain disposable, producing a readable write-run-inspect-repair loop for long-horizon web tasks.","impact":"Use Webwright to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (5,818 stars; 366 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;state","audience":"builder","loop_layer":"agent","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-08","publication_year":"2026","publication_venue":"microsoft/Webwright","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"microsoft/Webwright","github_stars":"5818","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0250","title":"Why Agentic Systems Must Produce Deterministic Outputs to Scale","url":"https://streamzero.com/blog/posts/deep-dives-tools-technologies-architectures/agentic-patterns/why-agentic-systems-must-produce-deterministic-outputs-to-scale","canonical_url":"https://streamzero.com/blog/posts/deep-dives-tools-technologies-architectures/agentic-patterns/why-agentic-systems-must-produce-deterministic-outputs-to-scale","annotation":"Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","key_contribution":"Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","impact":"Use Why Agentic Systems Must Produce Deterministic Outputs to Scale to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from streamzero.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"streamzero.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0251","title":"Stop Babysitting Your Coding Agent. Give It Backpressure.","url":"https://generativeprogrammer.com/p/stop-babysitting-your-coding-agent","canonical_url":"https://generativeprogrammer.com/p/stop-babysitting-your-coding-agent","annotation":"Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","key_contribution":"Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","impact":"Use Stop Babysitting Your Coding Agent. Give It Backpressure. to measure progress and gate completion with repeatable evidence.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;exit","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"operational-pattern","evidence_tier":"B","signal_strength":"medium","source_status":"ok","authors":"Bilgin Ibryam","publication_date":"","publication_year":"","publication_venue":"","publisher":"generativeprogrammer.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0252","title":"How to Build a Self-Verification Loop in Claude Code","url":"https://dev.to/shipwithaiio/how-to-build-a-self-verification-loop-in-claude-code-3-layers-20-minutes-m1p","canonical_url":"https://dev.to/shipwithaiio/how-to-build-a-self-verification-loop-in-claude-code-3-layers-20-minutes-m1p","annotation":"Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","key_contribution":"Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","novelty":"The agent workflow includes explicit self-checking or gated completion. Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","impact":"Use How to Build a Self-Verification Loop in Claude Code to measure progress and gate completion with repeatable evidence.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"operational-pattern","evidence_tier":"B","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"DEV Community","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0253","title":"Agentic Code Review","url":"https://addyosmani.com/blog/agentic-code-review/","canonical_url":"https://addyosmani.com/blog/agentic-code-review/","annotation":"Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","key_contribution":"Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","novelty":"Verification is promoted from a final check to a loop-control signal. Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","impact":"Use Agentic Code Review to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Addy Osmani","publication_date":"","publication_year":"","publication_venue":"","publisher":"addyosmani.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0254","title":"Using DSPy to Evaluate and Improve Datasette Agent's SQL System Prompts","url":"https://simonwillison.net/2026/Jul/2/dspy-datasette-agent-prompts/","canonical_url":"https://simonwillison.net/2026/Jul/2/dspy-datasette-agent-prompts/","annotation":"Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","key_contribution":"Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","impact":"Use Using DSPy to Evaluate and Improve Datasette Agent's SQL System Prompts to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0255","title":"Agentic coding notes","url":"https://danluu.com/ai-coding/","canonical_url":"https://danluu.com/ai-coding/","annotation":"Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","key_contribution":"Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","novelty":"The work turns loop quality into a measurable task or score. Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","impact":"Use Agentic coding notes to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from danluu.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"danluu.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0256","title":"Understanding Is the New Bottleneck","url":"https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html","canonical_url":"https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html","annotation":"Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","key_contribution":"Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","novelty":"Verification is promoted from a final check to a loop-control signal. Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","impact":"Use Understanding Is the New Bottleneck to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.geoffreylitt.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"geoffreylitt.com","doi":"","publication_note":"","primary_category":"","metadata_source":"url-date","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0257","title":"Verifying Agentic Development at Scale","url":"https://cognition.com/blog/testing-development","canonical_url":"https://cognition.com/blog/testing-development","annotation":"Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","key_contribution":"Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","novelty":"Verification is promoted from a final check to a loop-control signal. Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","impact":"Use Verifying Agentic Development at Scale to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-05-29","publication_year":"2026","publication_venue":"","publisher":"cognition.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0258","title":"Loop Engineering Without Verification Is Just Automation","url":"https://www.sonarsource.com/blog/loop-engineering-without-verification-is-just-automation/","canonical_url":"https://www.sonarsource.com/blog/loop-engineering-without-verification-is-just-automation/","annotation":"Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","key_contribution":"Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","novelty":"Verification is promoted from a final check to a loop-control signal. Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","impact":"Use Loop Engineering Without Verification Is Just Automation to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.sonarsource.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"sonarsource.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0259","title":"Closing the Verification Loop: Observability-Driven Harnesses","url":"https://www.datadoghq.com/blog/ai/harness-first-agents/","canonical_url":"https://www.datadoghq.com/blog/ai/harness-first-agents/","annotation":"Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","key_contribution":"Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","novelty":"Verification is promoted from a final check to a loop-control signal. Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","impact":"Use Closing the Verification Loop: Observability-Driven Harnesses to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.datadoghq.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Alp Keles, Jai Menon, Sesh Nalla, Vyom Shah","publication_date":"2026-03-09","publication_year":"2026","publication_venue":"","publisher":"Datadog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0260","title":"How to build a better agent harness with traces and evals","url":"https://arize.com/blog/improve-ai-agents-traces-evals-harness/","canonical_url":"https://arize.com/blog/improve-ai-agents-traces-evals-harness/","annotation":"Trace-evaluate-debug-refine loop for improving agent behavior from real runs.","key_contribution":"Trace-evaluate-debug-refine loop for improving agent behavior from real runs.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Trace-evaluate-debug-refine loop for improving agent behavior from real runs.","impact":"Use How to build a better agent harness with traces and evals to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from arize.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Aaron Winston","publication_date":"2026-05-29","publication_year":"2026","publication_venue":"","publisher":"Arize AI","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0261","title":"Better Harness: A Recipe for Harness Hill-Climbing with Evals","url":"https://www.langchain.com/blog/better-harness-a-recipe-for-harness-hill-climbing-with-evals","canonical_url":"https://www.langchain.com/blog/better-harness-a-recipe-for-harness-hill-climbing-with-evals","annotation":"LangChain's recipe for using evals as the learning signal for harness improvement.","key_contribution":"LangChain's recipe for using evals as the learning signal for harness improvement.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. LangChain's recipe for using evals as the learning signal for harness improvement.","impact":"Use Better Harness: A Recipe for Harness Hill-Climbing with Evals to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"LangChain","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0262","title":"Improving Deep Agents with harness engineering","url":"https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering","canonical_url":"https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering","annotation":"Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","key_contribution":"Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","novelty":"The agent workflow includes explicit self-checking or gated completion. Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","impact":"Use Improving Deep Agents with harness engineering to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"LangChain","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0263","title":"Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses","url":"https://arxiv.org/abs/2604.25850","canonical_url":"https://arxiv.org/abs/2604.25850","annotation":"Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","key_contribution":"Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","novelty":"Verification is promoted from a final check to a loop-control signal. Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","impact":"Use Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2604.25850; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jiahang Lin; Shichun Liu; Chengjun Pan; Lizhi Lin; Shihan Dou; Zhiheng Xi; Xuanjing Huang; Hang Yan; Zhenhua Han; Tao Gui; Yu-Gang Jiang","publication_date":"2026-04-28","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.25850","date_added":""},{"row_id":"ale-0264","title":"Meta-Harness: End-to-End Optimization of Model Harnesses","url":"https://arxiv.org/abs/2603.28052","canonical_url":"https://arxiv.org/abs/2603.28052","annotation":"Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","key_contribution":"Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","impact":"Use Meta-Harness: End-to-End Optimization of Model Harnesses to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2603.28052; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yoonho Lee; Roshen Nair; Qizheng Zhang; Kangwook Lee; Omar Khattab; Chelsea Finn","publication_date":"2026-03-30","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.28052","date_added":""},{"row_id":"ale-0265","title":"HALO (Hierarchical Agent Loop Optimizer)","url":"https://github.com/context-labs/halo","canonical_url":"https://github.com/context-labs/halo","annotation":"Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","key_contribution":"Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","impact":"Use HALO (Hierarchical Agent Loop Optimizer) to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (1,107 stars; 80 forks; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-21","publication_year":"2026","publication_venue":"context-labs/halo","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"context-labs/halo","github_stars":"1107","arxiv_id":"","date_added":""},{"row_id":"ale-0266","title":"Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions","url":"https://arxiv.org/abs/2607.03935","canonical_url":"https://arxiv.org/abs/2607.03935","annotation":"Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","key_contribution":"Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","impact":"Use Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.03935; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Haochen Luo; Yi Huang; Sichun Luo; Fengyuan Liu; Lei Li; Zefa Hu; Junlan Feng; Qi Liu","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.03935","date_added":""},{"row_id":"ale-0267","title":"auto-harness","url":"https://github.com/neosigmaai/auto-harness","canonical_url":"https://github.com/neosigmaai/auto-harness","annotation":"Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","key_contribution":"Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","impact":"Use auto-harness to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (525 stars; 59 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-03","publication_year":"2026","publication_venue":"neosigmaai/auto-harness","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"neosigmaai/auto-harness","github_stars":"525","arxiv_id":"","date_added":""},{"row_id":"ale-0268","title":"OpenAI agent evals","url":"https://developers.openai.com/api/docs/guides/agent-evals","canonical_url":"https://developers.openai.com/api/docs/guides/agent-evals","annotation":"Evaluation guidance for moving from traces to repeatable grading of agent workflows.","key_contribution":"Evaluation guidance for moving from traces to repeatable grading of agent workflows.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. 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Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.","impact":"Use Arize Phoenix to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (10,609 stars; 993 forks; NOASSERTION license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2022-11-09","publication_year":"2022","publication_venue":"Arize-ai/phoenix","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Arize-ai/phoenix","github_stars":"10609","arxiv_id":"","date_added":""},{"row_id":"ale-0276","title":"Braintrust","url":"https://www.braintrust.dev/","canonical_url":"https://www.braintrust.dev/","annotation":"Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","key_contribution":"Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","impact":"Use Braintrust to measure progress and gate completion with repeatable evidence.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"implementation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Braintrust","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0277","title":"Weave","url":"https://docs.wandb.ai/weave","canonical_url":"https://docs.wandb.ai/weave","annotation":"Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.","key_contribution":"Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.","impact":"Use Weave to measure progress and gate completion with repeatable evidence.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"implementation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Weights & Biases Documentation","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0278","title":"agentops (boshu2)","url":"https://github.com/boshu2/agentops","canonical_url":"https://github.com/boshu2/agentops","annotation":"Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","key_contribution":"Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","novelty":"Verification is promoted from a final check to a loop-control signal. Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","impact":"Use agentops (boshu2) to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (412 stars; 40 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;exit","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-11-05","publication_year":"2025","publication_venue":"boshu2/agentops","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"boshu2/agentops","github_stars":"412","arxiv_id":"","date_added":""},{"row_id":"ale-0279","title":"SkillSpec","url":"https://github.com/modiqo/skillspec","canonical_url":"https://github.com/modiqo/skillspec","annotation":"CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.","key_contribution":"CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.","impact":"Use SkillSpec to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (984 stars; 60 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-19","publication_year":"2026","publication_venue":"modiqo/skillspec","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"modiqo/skillspec","github_stars":"984","arxiv_id":"","date_added":""},{"row_id":"ale-0280","title":"Shepherd","url":"https://github.com/shepherd-agents/shepherd","canonical_url":"https://github.com/shepherd-agents/shepherd","annotation":"Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","key_contribution":"Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","impact":"Use Shepherd to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (1,458 stars; 105 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;state","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"shepherd-agents/shepherd","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"shepherd-agents/shepherd","github_stars":"1458","arxiv_id":"","date_added":""},{"row_id":"ale-0281","title":"grill-for-unknowns","url":"https://github.com/nicobailon/grill-for-unknowns","canonical_url":"https://github.com/nicobailon/grill-for-unknowns","annotation":"Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","key_contribution":"Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","novelty":"Verification is promoted from a final check to a loop-control signal. Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","impact":"Use grill-for-unknowns to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (179 stars; 6 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"nicobailon/grill-for-unknowns","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"nicobailon/grill-for-unknowns","github_stars":"179","arxiv_id":"","date_added":""},{"row_id":"ale-0282","title":"Fable Harness","url":"https://github.com/Miguok/fable-harness","canonical_url":"https://github.com/Miguok/fable-harness","annotation":"Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","key_contribution":"Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","impact":"Use Fable Harness to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (191 stars; 33 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;exit","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-05","publication_year":"2026","publication_venue":"Miguok/fable-harness","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Miguok/fable-harness","github_stars":"191","arxiv_id":"","date_added":""},{"row_id":"ale-0283","title":"Mindwalk","url":"https://github.com/cosmtrek/mindwalk","canonical_url":"https://github.com/cosmtrek/mindwalk","annotation":"Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","key_contribution":"Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","impact":"Use Mindwalk to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (780 stars; 47 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"cosmtrek/mindwalk","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"cosmtrek/mindwalk","github_stars":"780","arxiv_id":"","date_added":""},{"row_id":"ale-0284","title":"Waggle","url":"https://github.com/modiqo/waggle","canonical_url":"https://github.com/modiqo/waggle","annotation":"MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","key_contribution":"MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","novelty":"Context is managed as durable loop state rather than a single prompt payload. MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","impact":"Use Waggle to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (821 stars; 141 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"context;delegation;budget","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"modiqo/waggle","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"modiqo/waggle","github_stars":"821","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0285","title":"Jacquard","url":"https://github.com/jbwinters/jacquard-lang","canonical_url":"https://github.com/jbwinters/jacquard-lang","annotation":"Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","key_contribution":"Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","impact":"Use Jacquard to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (93 stars; 2 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"jbwinters/jacquard-lang","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"jbwinters/jacquard-lang","github_stars":"93","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0286","title":"Agentic Verification of Software Systems","url":"https://arxiv.org/abs/2511.17330","canonical_url":"https://doi.org/10.1145/3808164","annotation":"Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","key_contribution":"Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","novelty":"Verification is promoted from a final check to a loop-control signal. Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","impact":"Use Agentic Verification of Software Systems to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2511.17330; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Haoxin Tu; Huan Zhao; Yahui Song; Mehtab Zafar; Ruijie Meng; Abhik Roychoudhury","publication_date":"2026-06-30","publication_year":"2026","publication_venue":"Proceedings of the ACM on Software Engineering 3 (FSE)","publisher":"Association for Computing Machinery","doi":"10.1145/3808164","publication_note":"Published in Proceedings of the ACM on Software Engineering 3 (FSE); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ACM DOI record","github_repo":"","github_stars":"","arxiv_id":"2511.17330","date_added":""},{"row_id":"ale-0287","title":"A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance","url":"https://arxiv.org/abs/2603.18096","canonical_url":"https://doi.org/10.5220/0014840300004015","annotation":"Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","key_contribution":"Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","novelty":"Orchestration and control flow are made explicit and inspectable. Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","impact":"Use A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2603.18096; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Ciprian Paduraru; Petru-Liviu Bouruc; Alin Stefanescu","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 21st International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE)","publisher":"SCITEPRESS","doi":"10.5220/0014840300004015","publication_note":"Published in Proceedings of the 21st International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE); the linked arXiv record remains available for open access.","primary_category":"cs.MA","metadata_source":"SCITEPRESS DOI record","github_repo":"","github_stars":"","arxiv_id":"2603.18096","date_added":""},{"row_id":"ale-0288","title":"Self-Evolving Agents with Anytime-Valid Certificates","url":"https://arxiv.org/abs/2607.00871","canonical_url":"https://arxiv.org/abs/2607.00871","annotation":"Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","key_contribution":"Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","novelty":"Verification is promoted from a final check to a loop-control signal. Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","impact":"Use Self-Evolving Agents with Anytime-Valid Certificates to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.00871; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Biswa Sengupta","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00871","date_added":""},{"row_id":"ale-0289","title":"Delayed Verification Destabilizes Multi-Agent LLM Belief","url":"https://arxiv.org/abs/2606.27409","canonical_url":"https://arxiv.org/abs/2606.27409","annotation":"Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","key_contribution":"Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","novelty":"Verification is promoted from a final check to a loop-control signal. Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","impact":"Use Delayed Verification Destabilizes Multi-Agent LLM Belief to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2606.27409; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Igor Itkin","publication_date":"2026-06-25","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"20 pages, 5 figures, 1 table. Code and data: https://github.com/YehudaItkin/delayed-verification-llm","primary_category":"cs.MA","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.27409","date_added":""},{"row_id":"ale-0290","title":"Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory","url":"https://arxiv.org/abs/2606.06523","canonical_url":"https://arxiv.org/abs/2606.06523","annotation":"Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","key_contribution":"Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","novelty":"Verification is promoted from a final check to a loop-control signal. Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","impact":"Use Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2606.06523; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Ruida Wang; Jerry Huang; Pengcheng Wang; Xuanqing Liu; Luyang Kong; Tong Zhang","publication_date":"2026-06-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.06523","date_added":""},{"row_id":"ale-0291","title":"Regimes: An Auditable, Held-Out-Gated Improvement Loop","url":"https://arxiv.org/abs/2606.10241","canonical_url":"https://arxiv.org/abs/2606.10241","annotation":"Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","key_contribution":"Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","impact":"Use Regimes: An Auditable, Held-Out-Gated Improvement Loop to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2606.10241; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yohei Nakajima","publication_date":"2026-06-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"30 pages, 5 figures. Code and committed runs: https://github.com/yoheinakajima/regimes","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.10241","date_added":""},{"row_id":"ale-0292","title":"Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents","url":"https://arxiv.org/abs/2605.22608","canonical_url":"https://aclanthology.org/2026.acl-demo.74/","annotation":"Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","key_contribution":"Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","impact":"Use Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2605.22608; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Asaf Yehudai; Lilach Eden; Michal Shmueli-Scheuer","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL)","publisher":"Association for Computational Linguistics","doi":"10.18653/v1/2026.acl-demo.74","publication_note":"Published in Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"ACL Anthology and DOI records","github_repo":"","github_stars":"","arxiv_id":"2605.22608","date_added":""},{"row_id":"ale-0293","title":"Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference","url":"https://arxiv.org/abs/2607.02882","canonical_url":"https://arxiv.org/abs/2607.02882","annotation":"FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","key_contribution":"FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","impact":"Use Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.02882; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xuyan Ma; Yawen Wang; Junjie Wang; Xiaofei Xie; Boyu Wu; Mingyang Li; Dandan Wang; Qing Wang","publication_date":"2026-07-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02882","date_added":""},{"row_id":"ale-0294","title":"SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use","url":"https://arxiv.org/abs/2607.01874","canonical_url":"https://arxiv.org/abs/2607.01874","annotation":"Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","key_contribution":"Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","impact":"Use SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.01874; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jiayin Zhu; Kelong Mao; Yudong Guo; Dengbo He; Sulong Xu; Simiu Gu; Yutao Yue","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.01874","date_added":""},{"row_id":"ale-0295","title":"SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Bug Reproduction Tests","url":"https://arxiv.org/abs/2607.00990","canonical_url":"https://arxiv.org/abs/2607.00990","annotation":"Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","key_contribution":"Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","novelty":"Verification is promoted from a final check to a loop-control signal. Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","impact":"Use SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Bug Reproduction Tests to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.00990; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yaoqi Guo; Yang Liu; Jie M. Zhang; Yun Ma; Yiling Lou; Zhenpeng Chen","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00990","date_added":""},{"row_id":"ale-0296","title":"AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation","url":"https://arxiv.org/abs/2607.06273","canonical_url":"https://arxiv.org/abs/2607.06273","annotation":"Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","key_contribution":"Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","impact":"Use AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.06273; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Chenyu Zhao; Shenglin Zhang; Wenwei Gu; Yongqian Sun; Dan Pei; Chetan Bansal; Saravan Rajmohan; Minghua Ma","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06273","date_added":""},{"row_id":"ale-0297","title":"SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review","url":"https://arxiv.org/abs/2607.06065","canonical_url":"https://arxiv.org/abs/2607.06065","annotation":"Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","key_contribution":"Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","novelty":"Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","impact":"Use SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.06065; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Ruoyu Wang; Jierun Chen; Shaowei Wang; Chaofan Tao; Sidi Yang; Yuxin Jiang; Kim-Hui Yap; Lifeng Shang; Xiaohui Li; Haoli Bai","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06065","date_added":""},{"row_id":"ale-0298","title":"Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode","url":"https://arxiv.org/abs/2607.07405","canonical_url":"https://arxiv.org/abs/2607.07405","annotation":"Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","key_contribution":"Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","novelty":"State persistence is explicit enough for repeated runs and handoff. Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","impact":"Use Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07405; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Vikas Reddy; Sumanth Reddy Challaram; Abhishek Basu","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07405","date_added":""},{"row_id":"ale-0299","title":"Harnessing Code Agents for Automatic Software Verification","url":"https://arxiv.org/abs/2607.06341","canonical_url":"https://arxiv.org/abs/2607.06341","annotation":"Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","key_contribution":"Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","novelty":"Verification is promoted from a final check to a loop-control signal. Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","impact":"Use Harnessing Code Agents for Automatic Software Verification to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.06341; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Shuangxiang Kan; Shuanglong Kan; Sebastian Ertel","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.FL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06341","date_added":""},{"row_id":"ale-0300","title":"LLM-as-a-Verifier: A General-Purpose Verification Framework","url":"https://arxiv.org/abs/2607.05391","canonical_url":"https://arxiv.org/abs/2607.05391","annotation":"Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","key_contribution":"Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","novelty":"Verification is promoted from a final check to a loop-control signal. Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","impact":"Use LLM-as-a-Verifier: A General-Purpose Verification Framework to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.05391; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;budget","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jacky Kwok; Shulu Li; Pranav Atreya; Yuejiang Liu; Yixing Jiang; Chelsea Finn; Marco Pavone; Ion Stoica; Azalia Mirhoseini","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code: https://github.com/llm-as-a-verifier/llm-as-a-verifier Website: https://llm-as-a-verifier.com","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05391","date_added":""},{"row_id":"ale-0301","title":"From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents","url":"https://arxiv.org/abs/2607.08028","canonical_url":"https://arxiv.org/abs/2607.08028","annotation":"Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","key_contribution":"Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","impact":"Use From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.08028; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Joongho Ahn; Moonsoo Kim","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"32 pages, 6 figures, 16 tables. Reference implementation and evaluation artifacts: https://github.com/hammerbaki/enterprise-llm-agent-harness (archived at https://doi.org/10.5281/zenodo.21269426)","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08028","date_added":""},{"row_id":"ale-0302","title":"From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization","url":"https://arxiv.org/abs/2607.07702","canonical_url":"https://arxiv.org/abs/2607.07702","annotation":"STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","key_contribution":"STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","impact":"Use From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07702; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Ying Chang; Jiahang Xu; Xuan Feng; Chenyuan Yang; Peng Cheng; Yuqing Yang","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07702","date_added":""},{"row_id":"ale-0303","title":"Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems","url":"https://arxiv.org/abs/2607.07989","canonical_url":"https://arxiv.org/abs/2607.07989","annotation":"AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","key_contribution":"AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","novelty":"Verification is promoted from a final check to a loop-control signal. AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","impact":"Use Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07989; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yufei Xia; Anjun Gao; Yueyang Quan; Zhuqing Liu; Minghong Fang","publication_date":"2026","publication_year":"2026","publication_venue":"Conference on Language Modeling (COLM)","publisher":"Conference on Language Modeling","doi":"","publication_note":"Accepted at Conference on Language Modeling (COLM); the linked arXiv record is the available paper version.","primary_category":"cs.CR","metadata_source":"Official COLM accepted-papers list and current arXiv note","github_repo":"","github_stars":"","arxiv_id":"2607.07989","date_added":""},{"row_id":"ale-0304","title":"3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse","url":"https://arxiv.org/abs/2607.07980","canonical_url":"https://arxiv.org/abs/2607.07980","annotation":"Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","key_contribution":"Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","impact":"Use 3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07980; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"context","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Shyam Agarwal; Courtney Miller; Christian Kästner; Bogdan Vasilescu","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07980","date_added":""},{"row_id":"ale-0305","title":"Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring","url":"https://arxiv.org/abs/2607.08066","canonical_url":"https://arxiv.org/abs/2607.08066","annotation":"Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","key_contribution":"Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","impact":"Use Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.08066; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jennifer Za; Julija Bainiaksina; Nikita Ostrovsky; Tanush Chopra; Victoria Krakovna","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"25 pages, 10 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08066","date_added":""},{"row_id":"ale-0306","title":"Physics-Audited Agentic Discovery in Scientific Machine Learning","url":"https://arxiv.org/abs/2607.07379","canonical_url":"https://arxiv.org/abs/2607.07379","annotation":"Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","key_contribution":"Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","novelty":"Verification is promoted from a final check to a loop-control signal. Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","impact":"Use Physics-Audited Agentic Discovery in Scientific Machine Learning to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07379; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Diab W. Abueidda; Bilal Ahmed; Panos Pantidis; Mostafa E. Mobasher","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07379","date_added":""},{"row_id":"ale-0307","title":"Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair","url":"https://arxiv.org/abs/2607.07882","canonical_url":"https://arxiv.org/abs/2607.07882","annotation":"TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","key_contribution":"TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","novelty":"The resource is directly reusable as a starting artifact. TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","impact":"Use Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07882; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"S M Farah Al Fahim; Md Nakhla Rafi; Md Ahasanuzzaman; Zeyang Ma; Dong Jae Kim; Shaowei Wang; Tse-Hsun; Chen","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07882","date_added":""},{"row_id":"ale-0308","title":"Failure as a Process: An Anatomy of CLI Coding Agent Trajectories","url":"https://arxiv.org/abs/2607.09510","canonical_url":"https://arxiv.org/abs/2607.09510","annotation":"Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","key_contribution":"Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","impact":"Use Failure as a Process: An Anatomy of CLI Coding Agent Trajectories to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.09510; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xiangxin Zhao; Han Li; Shuaiting Li; Tianyi Zhao; Earl T. Barr; Federica Sarro; He Ye","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"12 pages, 6 figures","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09510","date_added":""},{"row_id":"ale-0309","title":"Agentic Proof and Property-Based Testing via Property-Templates","url":"https://arxiv.org/abs/2607.09072","canonical_url":"https://arxiv.org/abs/2607.09072","annotation":"Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","key_contribution":"Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","novelty":"Verification is promoted from a final check to a loop-control signal. Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","impact":"Use Agentic Proof and Property-Based Testing via Property-Templates to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.09072; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Seongmin Lee; Yaoxuan Wu; Miryung Kim","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"12 pages, 7 figures, 4 tables; supplementary material included as ancillary file","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09072","date_added":""},{"row_id":"ale-0310","title":"AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP","url":"https://arxiv.org/abs/2607.11098","canonical_url":"https://arxiv.org/abs/2607.11098","annotation":"Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","key_contribution":"Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","impact":"Use AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.11098; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Aritra Mazumder; Nusrat jahan Lia","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11098","date_added":"2026-07-15"},{"row_id":"ale-0311","title":"Latent Programming Horizons in Coding Agents","url":"https://arxiv.org/abs/2607.05188","canonical_url":"https://arxiv.org/abs/2607.05188","annotation":"Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","key_contribution":"Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","novelty":"Verification is promoted from a final check to a loop-control signal. Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","impact":"Use Latent Programming Horizons in Coding Agents to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.05188; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"André Silva; Han Tu; Martin Monperrus","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05188","date_added":"2026-07-15"},{"row_id":"ale-0312","title":"Why evaluate agents","url":"https://adk.dev/evaluate/","canonical_url":"https://adk.dev/evaluate/","annotation":"Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","key_contribution":"Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","novelty":"Primary-source operational guidance rather than commentary. Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","impact":"Use Why evaluate agents to measure progress and gate completion with repeatable evidence.","signal":"Primary official documentation from adk.dev; use it for current product or standard behavior.","resource_type":"Docs","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"builder;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Google Agent Development Kit","publication_date":"","publication_year":"","publication_venue":"Google Agent Development Kit","publisher":"Google","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0313","title":"Structured Feedback Improves Repair in an LLM Agent Loop","url":"https://arxiv.org/abs/2607.14167","canonical_url":"https://arxiv.org/abs/2607.14167","annotation":"In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","key_contribution":"In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","novelty":"The contribution is machine-readable and validation-friendly. In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","impact":"Use Structured Feedback Improves Repair in an LLM Agent Loop to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.14167; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"budget","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jaideep Ray; Ankit Goyal","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14167","date_added":"2026-07-17"},{"row_id":"ale-0314","title":"Copy-on-Write Scoring: Application-Specific Agent Evaluations","url":"https://arxiv.org/abs/2607.14336","canonical_url":"https://arxiv.org/abs/2607.14336","annotation":"Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.","key_contribution":"Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.","novelty":"State persistence is explicit enough for repeated runs and handoff. Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.","impact":"Use Copy-on-Write Scoring: Application-Specific Agent Evaluations to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.14336; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Joanna Roy; Sven Hoelzel","publication_date":"2026","publication_year":"2026","publication_venue":"ICML Workshop on Agents in the Wild: Safety Security and Beyond","publisher":"International Conference on Machine Learning","doi":"","publication_note":"Accepted at ICML Workshop on Agents in the Wild: Safety Security and Beyond; the linked arXiv record is the available paper version.","primary_category":"cs.SE","metadata_source":"Current arXiv acceptance note and official workshop page","github_repo":"","github_stars":"","arxiv_id":"2607.14336","date_added":"2026-07-17"},{"row_id":"ale-0315","title":"The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK","url":"https://arxiv.org/abs/2607.14340","canonical_url":"https://arxiv.org/abs/2607.14340","annotation":"Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","key_contribution":"Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","novelty":"Verification is promoted from a final check to a loop-control signal. Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","impact":"Use The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.14340; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Tobias Philipp","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14340","date_added":"2026-07-17"},{"row_id":"ale-0316","title":"GLEAN: Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification","url":"https://arxiv.org/abs/2603.02798","canonical_url":"https://arxiv.org/abs/2603.02798","annotation":"Compiles expert guidelines into step-wise trajectory checks, calibrates accumulated evidence into correctness probabilities, and triggers additional verification when uncertainty remains high.","key_contribution":"Compiles expert guidelines into step-wise trajectory checks, calibrates accumulated evidence into correctness probabilities, and triggers additional verification when uncertainty remains high.","novelty":"Verification is promoted from a final check to a loop-control signal. Compiles expert guidelines into step-wise trajectory checks, calibrates accumulated evidence into correctness probabilities, and triggers additional verification when uncertainty remains high.","impact":"Use GLEAN: Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2603.02798; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"trigger;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yichi Zhang; Nabeel Seedat; Yinpeng Dong; Peng Cui; Jun Zhu; Mihaela van de Schaar","publication_date":"2026-03-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.02798","date_added":"2026-07-18"},{"row_id":"ale-0317","title":"Zombie Agents: Detecting Semantic Livelock in Long-Horizon Autonomous Software","url":"https://doi.org/10.1145/3805760.3814895","canonical_url":"https://doi.org/10.1145/3805760.3814895","annotation":"Defines semantic livelock as continued agent activity without progress and proposes an independent embedding-based convergence monitor that detected the pattern in 25% of the analyzed long-duration SWE-agent failures.","key_contribution":"Defines semantic livelock as continued agent activity without progress and proposes an independent embedding-based convergence monitor that detected the pattern in 25% of the analyzed long-duration SWE-agent failures.","novelty":"The work targets tasks that exceed a single context window or prompt session. Defines semantic livelock as continued agent activity without progress and proposes an independent embedding-based convergence monitor that detected the pattern in 25% of the analyzed long-duration SWE-agent failures.","impact":"Use Zombie Agents: Detecting Semantic Livelock in Long-Horizon Autonomous Software to measure progress and gate completion with repeatable evidence.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"restricted","authors":"Simarjot Khanna","publication_date":"2026-07","publication_year":"2026","publication_venue":"Proceedings of the 3rd ACM International Conference on AI-Powered Software (AIware '26)","publisher":"Association for Computing Machinery","doi":"10.1145/3805760.3814895","publication_note":"Published at AIware 2026; metadata verified from the author-supplied camera-ready paper because the DOI landing page restricted automated access.","primary_category":"","metadata_source":"ACM DOI and camera-ready paper","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0318","title":"The lethal trifecta for AI agents","url":"https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/","canonical_url":"https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/","annotation":"Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","key_contribution":"Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","novelty":"Untrusted intake is treated as a loop-level security boundary. Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","impact":"Use The lethal trifecta for AI agents to bound risk before recurring or unattended execution.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"risk-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"2025","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0319","title":"Prompt injection series","url":"https://simonwillison.net/series/prompt-injection/","canonical_url":"https://simonwillison.net/series/prompt-injection/","annotation":"Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.","key_contribution":"Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.","novelty":"Untrusted intake is treated as a loop-level security boundary. Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.","impact":"Use Prompt injection series to bound risk before recurring or unattended execution.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"intake","audience":"operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"risk-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0320","title":"Agentic AI - Threats and Mitigations","url":"https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/","canonical_url":"https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/","annotation":"OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","key_contribution":"OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","novelty":"Persistent memory is treated as an external runtime artifact. OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","impact":"Use Agentic AI - Threats and Mitigations to bound risk before recurring or unattended execution.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"intake;workspace;context;delegation","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"technical-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"OWASPGenAIProject Editor","publication_date":"","publication_year":"","publication_venue":"","publisher":"OWASP Gen AI Security Project","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0321","title":"Designing AI agents to resist prompt injection","url":"https://openai.com/index/designing-agents-to-resist-prompt-injection/","canonical_url":"https://openai.com/index/designing-agents-to-resist-prompt-injection/","annotation":"OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","key_contribution":"OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","novelty":"Primary-source operational guidance rather than commentary. OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","impact":"Use Designing AI agents to resist prompt injection to bound risk before recurring or unattended execution.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;verification;escalation","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"technical-documentation","evidence_tier":"A","signal_strength":"high","source_status":"restricted","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"OpenAI","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0322","title":"sandbox-runtime","url":"https://github.com/anthropic-experimental/sandbox-runtime","canonical_url":"https://github.com/anthropic-experimental/sandbox-runtime","annotation":"Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.","key_contribution":"Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.","novelty":"Execution isolation and permission boundaries are part of the design. Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.","impact":"Use sandbox-runtime to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (4,695 stars; 364 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-10-20","publication_year":"2025","publication_venue":"anthropic-experimental/sandbox-runtime","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"anthropic-experimental/sandbox-runtime","github_stars":"4695","arxiv_id":"","date_added":""},{"row_id":"ale-0323","title":"E2B","url":"https://github.com/e2b-dev/E2B","canonical_url":"https://github.com/e2b-dev/E2B","annotation":"Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","key_contribution":"Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","novelty":"Execution isolation and permission boundaries are part of the design. Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","impact":"Use E2B to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (13,022 stars; 967 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-03-04","publication_year":"2023","publication_venue":"e2b-dev/E2B","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"e2b-dev/E2B","github_stars":"13022","arxiv_id":"","date_added":""},{"row_id":"ale-0324","title":"Modal Sandboxes","url":"https://modal.com/docs/guide/sandboxes","canonical_url":"https://modal.com/docs/guide/sandboxes","annotation":"Secure sandboxed execution for agent-driven code with resource limits and network controls.","key_contribution":"Secure sandboxed execution for agent-driven code with resource limits and network controls.","novelty":"Execution isolation and permission boundaries are part of the design. Secure sandboxed execution for agent-driven code with resource limits and network controls.","impact":"Use Modal Sandboxes to bound risk before recurring or unattended execution.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"technical-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Modal","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0325","title":"Daytona","url":"https://www.daytona.io/","canonical_url":"https://www.daytona.io/","annotation":"Infrastructure for running AI-generated code in fast, isolated sandboxes.","key_contribution":"Infrastructure for running AI-generated code in fast, isolated sandboxes.","novelty":"Execution isolation and permission boundaries are part of the design. Infrastructure for running AI-generated code in fast, isolated sandboxes.","impact":"Use Daytona to bound risk before recurring or unattended execution.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"implementation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"daytona.io","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0326","title":"peerd","url":"https://github.com/NotASithLord/peerd","canonical_url":"https://github.com/NotASithLord/peerd","annotation":"Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","key_contribution":"Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","novelty":"Orchestration and control flow are made explicit and inspectable. Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","impact":"Use peerd to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (361 stars; 35 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;delegation","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-22","publication_year":"2026","publication_venue":"NotASithLord/peerd","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"NotASithLord/peerd","github_stars":"361","arxiv_id":"","date_added":""},{"row_id":"ale-0327","title":"When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents","url":"https://arxiv.org/abs/2607.05189","canonical_url":"https://arxiv.org/abs/2607.05189","annotation":"Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","key_contribution":"Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","impact":"Use When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.05189; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"context;verification;state","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yechao Zhang; Shiqian Zhao; Jiawen Zhang; Jie Zhang; Gelei Deng; Xiaogeng Liu; Chaowei Xiao; Tianwei Zhang","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"25 pages, 8 figures. Preprint","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05189","date_added":""},{"row_id":"ale-0328","title":"Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses","url":"https://arxiv.org/abs/2607.05029","canonical_url":"https://arxiv.org/abs/2607.05029","annotation":"Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","key_contribution":"Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","impact":"Use Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.05029; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"context;verification;state","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Neeraj Karamchandani; Piyush Nagasubramaniam; Sencun Zhu; Dinghao Wu","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Preprint. 10 pages, 2 figures, 4 tables","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05029","date_added":""},{"row_id":"ale-0329","title":"Distributed Attacks in Persistent-State AI Control","url":"https://arxiv.org/abs/2607.02514","canonical_url":"https://arxiv.org/abs/2607.02514","annotation":"Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","key_contribution":"Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","impact":"Use Distributed Attacks in Persistent-State AI Control to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.02514; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"verification;state","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Josh Hills; Ida Caspary; Asa Cooper Stickland","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02514","date_added":""},{"row_id":"ale-0330","title":"ElephantAgent: Contextual State Continuity in Agentic Systems","url":"https://arxiv.org/abs/2607.01919","canonical_url":"https://arxiv.org/abs/2607.01919","annotation":"Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","key_contribution":"Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","novelty":"Verification is promoted from a final check to a loop-control signal. Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","impact":"Use ElephantAgent: Contextual State Continuity in Agentic Systems to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.01919; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context;verification;state","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jiankai Jin; Xiangzheng Zhang; Zhao Liu; Wenzhuo Xu; Dongdong Yang; Deyue Zhang; Quanchen Zou","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.01919","date_added":""},{"row_id":"ale-0331","title":"Cloudflare security-audit-skill","url":"https://github.com/cloudflare/security-audit-skill","canonical_url":"https://github.com/cloudflare/security-audit-skill","annotation":"Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","key_contribution":"Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","novelty":"The contribution is machine-readable and validation-friendly. Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","impact":"Use Cloudflare security-audit-skill to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (2,566 stars; 190 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"verification","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-18","publication_year":"2026","publication_venue":"cloudflare/security-audit-skill","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"cloudflare/security-audit-skill","github_stars":"2566","arxiv_id":"","date_added":""},{"row_id":"ale-0332","title":"The Balkanization of Execution-Security Research for AI Coding Agents","url":"https://arxiv.org/abs/2607.05743","canonical_url":"https://arxiv.org/abs/2607.05743","annotation":"Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","key_contribution":"Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","novelty":"Verification is promoted from a final check to a loop-control signal. Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","impact":"Use The Balkanization of Execution-Security Research for AI Coding Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.05743; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Mohammadreza Rashidi","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"18 pages, 15 figures, 6 tables. Systematizes 39 execution-security papers (2023-2026) into 17 verified categories. Machine-readable corpus and verification script released as a supplementary artifact","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05743","date_added":""},{"row_id":"ale-0333","title":"Context-to-Execution Integrity for LLM Agents","url":"https://arxiv.org/abs/2607.06000","canonical_url":"https://arxiv.org/abs/2607.06000","annotation":"Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","key_contribution":"Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","impact":"Use Context-to-Execution Integrity for LLM Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.06000; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Igor Santos-Grueiro","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"20 pages","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06000","date_added":""},{"row_id":"ale-0334","title":"When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents","url":"https://arxiv.org/abs/2607.06595","canonical_url":"https://arxiv.org/abs/2607.06595","annotation":"GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.","key_contribution":"GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.","novelty":"Persistent memory is treated as an external runtime artifact. GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.","impact":"Use When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.06595; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context;state","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"George Torres; Sharad Shrestha; Satyajayant Misra","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06595","date_added":""},{"row_id":"ale-0335","title":"Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents","url":"https://arxiv.org/abs/2607.08395","canonical_url":"https://arxiv.org/abs/2607.08395","annotation":"Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","key_contribution":"Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","novelty":"Persistent memory is treated as an external runtime artifact. Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","impact":"Use Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.08395; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context;state;budget","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Puji Wang; Yingchen Zhang; Ruqing Zhang; Jiafeng Guo; Xueqi Cheng","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08395","date_added":""},{"row_id":"ale-0336","title":"Prismata: Confining Cross-Site Prompt Injection in Web Agents","url":"https://arxiv.org/abs/2607.08147","canonical_url":"https://arxiv.org/abs/2607.08147","annotation":"Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","key_contribution":"Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","novelty":"Untrusted intake is treated as a loop-level security boundary. Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","impact":"Use Prismata: Confining Cross-Site Prompt Injection in Web Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.08147; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Corban Villa; Alp Eren Ozdarendeli; Sijun Tan; Raluca Ada Popa","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08147","date_added":""},{"row_id":"ale-0337","title":"TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories","url":"https://arxiv.org/abs/2607.08400","canonical_url":"https://arxiv.org/abs/2607.08400","annotation":"Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","key_contribution":"Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","novelty":"The work targets tasks that exceed a single context window or prompt session. Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","impact":"Use TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.08400; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Zheng Gao; Xiaoyu Li; Xiaoyan Feng; Jiaojiao Jiang; Yang Song; Yulei Sui; Zhenchang Xing; Liming Zhu","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08400","date_added":""},{"row_id":"ale-0338","title":"Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents","url":"https://arxiv.org/abs/2607.07474","canonical_url":"https://arxiv.org/abs/2607.07474","annotation":"Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","key_contribution":"Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","impact":"Use Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07474; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;escalation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Harry Owiredu-Ashley","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"8 pages, 6 figures. Code and artifacts: https://github.com/Harry-Ashley/action-graded-severity","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07474","date_added":""},{"row_id":"ale-0339","title":"Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting","url":"https://arxiv.org/abs/2607.07433","canonical_url":"https://arxiv.org/abs/2607.07433","annotation":"Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","key_contribution":"Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","impact":"Use Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07433; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Aya Spira; Stav Cohen; Elad Feldman; Ron Bitton; Avishai Wool; Ben Nassi","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Website: https://sites.google.com/view/agentic-botnets/home","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07433","date_added":""},{"row_id":"ale-0340","title":"GitLost: How We Tricked GitHub's AI Agent into Leaking Private Repos","url":"https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/","canonical_url":"https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/","annotation":"Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","key_contribution":"Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","impact":"Use GitLost: How We Tricked GitHub's AI Agent into Leaking Private Repos to bound risk before recurring or unattended execution.","signal":"Contextual source from noma.security; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"trigger;intake","audience":"operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"noma.security","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0341","title":"ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents","url":"https://arxiv.org/abs/2607.07774","canonical_url":"https://arxiv.org/abs/2607.07774","annotation":"Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","key_contribution":"Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","novelty":"The work turns loop quality into a measurable task or score. Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","impact":"Use ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents to bound risk before recurring or unattended execution.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;verification;budget","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Shane Caldwell; Max Harley; Ads Dawson; Michael Kouremetis; Vincent Abruzzo; Will Pearce","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"22 pages, 4 figures, 4 tables","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07774","date_added":""},{"row_id":"ale-0342","title":"Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors","url":"https://arxiv.org/abs/2607.07368","canonical_url":"https://arxiv.org/abs/2607.07368","annotation":"Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","key_contribution":"Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","impact":"Use Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07368; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"objective;delegation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Oliver Makins; Orazio Angelini; Zohreh Shams; Mary Phuong","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Submitted to NeurIPS; 81 pages; 32 figures and 24 tables","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07368","date_added":""},{"row_id":"ale-0343","title":"Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions","url":"https://arxiv.org/abs/2607.07461","canonical_url":"https://arxiv.org/abs/2607.07461","annotation":"Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","key_contribution":"Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","impact":"Use Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07461; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yang Shi; Jiaheng Fu; Yihe Huang; Ruixiang Wu; Chengyao Sun; Kaifeng Huang","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07461","date_added":""},{"row_id":"ale-0344","title":"Factory Droid Shield 2.0: Learned Secret Detection for Autonomous Commits","url":"https://factory.ai/news/droid-shield-2-0","canonical_url":"https://factory.ai/news/droid-shield-2-0","annotation":"Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","key_contribution":"Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","novelty":"Verification is promoted from a final check to a loop-control signal. Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","impact":"Use Factory Droid Shield 2.0: Learned Secret Detection for Autonomous Commits to bound risk before recurring or unattended execution.","signal":"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"verification","audience":"operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Factory","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"","publisher":"Factory","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0345","title":"destructive_command_guard","url":"https://github.com/Dicklesworthstone/destructive_command_guard","canonical_url":"https://github.com/Dicklesworthstone/destructive_command_guard","annotation":"Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","key_contribution":"Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","impact":"Use destructive_command_guard to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (5,086 stars; 192 forks; NOASSERTION license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-07","publication_year":"2026","publication_venue":"Dicklesworthstone/destructive_command_guard","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Dicklesworthstone/destructive_command_guard","github_stars":"5086","arxiv_id":"","date_added":""},{"row_id":"ale-0346","title":"Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution","url":"https://ainowinstitute.org/publications/friendly-fire-exploit-brief","canonical_url":"https://ainowinstitute.org/publications/friendly-fire-exploit-brief","annotation":"Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","key_contribution":"Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","novelty":"Untrusted intake is treated as a loop-level security boundary. Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","impact":"Use Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution to bound risk before recurring or unattended execution.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Boyan Milanov","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"","publisher":"AI Now Institute","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0347","title":"How We Contain Claude Across Products","url":"https://www.anthropic.com/engineering/how-we-contain-claude","canonical_url":"https://www.anthropic.com/engineering/how-we-contain-claude","annotation":"Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","key_contribution":"Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","novelty":"Execution isolation and permission boundaries are part of the design. Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","impact":"Use How We Contain Claude Across Products to bound risk before recurring or unattended execution.","signal":"Contextual source from www.anthropic.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0348","title":"Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Scanner Reliability","url":"https://arxiv.org/abs/2607.11086","canonical_url":"https://arxiv.org/abs/2607.11086","annotation":"Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","key_contribution":"Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","impact":"Use Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Scanner Reliability to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.11086; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Pei Chen; Baichao An; Mengying Wu; Binwang Wan; Geng Hong; Jinsong Chen; Xudong Pan; Jiarun Dai; Min Yang","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"18 pages, 11 figures, and 10 tables. This article substantially extends the preliminary 3-page MCPZoo dataset release arXiv:2512.15144. Includes appendices","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11086","date_added":"2026-07-15"},{"row_id":"ale-0349","title":"Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming","url":"https://arxiv.org/abs/2607.11698","canonical_url":"https://arxiv.org/abs/2607.11698","annotation":"Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","key_contribution":"Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","novelty":"Verification is promoted from a final check to a loop-control signal. Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","impact":"Use Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.11698; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"intake;verification","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xutao Mao; Xiang Zheng; Cong Wang","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11698","date_added":"2026-07-15"},{"row_id":"ale-0350","title":"Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents","url":"https://arxiv.org/abs/2607.10487","canonical_url":"https://arxiv.org/abs/2607.10487","annotation":"Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","key_contribution":"Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","impact":"Use Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.10487; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Igor Santos-Grueiro","publication_date":"2026-07-11","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"20 pages","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.10487","date_added":"2026-07-15"},{"row_id":"ale-0351","title":"ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm","url":"https://arxiv.org/abs/2607.10455","canonical_url":"https://openreview.net/forum?id=YqTodSrPPB","annotation":"Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","key_contribution":"Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","impact":"Use ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.10455; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Kefan Song; Yanjun Qi","publication_date":"2026-04-30","publication_year":"2026","publication_venue":"Proceedings of the 43rd International Conference on Machine Learning (ICML)","publisher":"PMLR","doi":"","publication_note":"Accepted at Proceedings of the 43rd International Conference on Machine Learning (ICML); the linked arXiv record is the available paper version.","primary_category":"cs.AI","metadata_source":"ICML OpenReview record","github_repo":"","github_stars":"","arxiv_id":"2607.10455","date_added":"2026-07-15"},{"row_id":"ale-0352","title":"Clawk","url":"https://github.com/clawkwork/clawk","canonical_url":"https://github.com/clawkwork/clawk","annotation":"Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","key_contribution":"Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","novelty":"Execution isolation and permission boundaries are part of the design. Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","impact":"Use Clawk to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (700 stars; 20 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"clawkwork/clawk","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"clawkwork/clawk","github_stars":"700","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0353","title":"Auto-Review of Agent Actions Without Synchronous Human Oversight","url":"https://alignment.openai.com/auto-review/","canonical_url":"https://alignment.openai.com/auto-review/","annotation":"OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","key_contribution":"OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","impact":"Use Auto-Review of Agent Actions Without Synchronous Human Oversight to bound risk before recurring or unattended execution.","signal":"Contextual source from alignment.openai.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"escalation","audience":"operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Maja Trębacz; Sam Arnesen; Ollie Matthews; Dylan Hurd; Won Park; Owen Lin; Joe Gershenson","publication_date":"2026-04-30","publication_year":"2026","publication_venue":"OpenAI","publisher":"OpenAI","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0354","title":"SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing","url":"https://arxiv.org/abs/2607.13594","canonical_url":"https://arxiv.org/abs/2607.13594","annotation":"Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","key_contribution":"Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","impact":"Use SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.13594; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"context;escalation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Tianyu Chen; Chujia Hu; Wenjie Wang","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13594","date_added":"2026-07-17"},{"row_id":"ale-0355","title":"CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems","url":"https://arxiv.org/abs/2607.13716","canonical_url":"https://arxiv.org/abs/2607.13716","annotation":"Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","key_contribution":"Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","novelty":"Verification is promoted from a final check to a loop-control signal. Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","impact":"Use CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.13716; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;verification;escalation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Zexun Wang","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"35 pages. Working paper on canonical action verification, runtime governance, semantic pattern detection, and approval-bound action receipts","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13716","date_added":"2026-07-17"},{"row_id":"ale-0356","title":"How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement","url":"https://arxiv.org/abs/2607.13718","canonical_url":"https://arxiv.org/abs/2607.13718","annotation":"Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","key_contribution":"Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","impact":"Use How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.13718; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Alexandra E. Michael; Franziska Roesner","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"15 pages, 4 figures","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13718","date_added":"2026-07-17"},{"row_id":"ale-0357","title":"Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives","url":"https://arxiv.org/abs/2607.14166","canonical_url":"https://arxiv.org/abs/2607.14166","annotation":"Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","key_contribution":"Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","impact":"Use Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.14166; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"verification;escalation;exit","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Sajjad Khan","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"31 pages, 3 figures, 11 tables","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14166","date_added":"2026-07-17"},{"row_id":"ale-0358","title":"Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems","url":"https://arxiv.org/abs/2607.14611","canonical_url":"https://arxiv.org/abs/2607.14611","annotation":"Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","key_contribution":"Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","novelty":"Persistent memory is treated as an external runtime artifact. Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","impact":"Use Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.14611; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"context;state","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Soham Gadgil; David Alexander; Sai Sunku; Franziska Roesner","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Preprint","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14611","date_added":"2026-07-17"},{"row_id":"ale-0359","title":"Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents","url":"https://arxiv.org/abs/2607.15143","canonical_url":"https://arxiv.org/abs/2607.15143","annotation":"Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","key_contribution":"Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","impact":"Use Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.15143; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Aadesh Bagmar; Pushkar Saraf","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.15143","date_added":"2026-07-17"},{"row_id":"ale-0360","title":"OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows","url":"https://aclanthology.org/2026.acl-long.431/","canonical_url":"https://aclanthology.org/2026.acl-long.431/","annotation":"Combines a formal verifier for explicit system violations with a contextual VLM judge, backed by the MobileRisk-Live sandbox and trajectory benchmark for realistic mobile-agent safety.","key_contribution":"Combines a formal verifier for explicit system violations with a contextual VLM judge, backed by the MobileRisk-Live sandbox and trajectory benchmark for realistic mobile-agent safety.","novelty":"Verification is promoted from a final check to a loop-control signal. Combines a formal verifier for explicit system violations with a contextual VLM judge, backed by the MobileRisk-Live sandbox and trajectory benchmark for realistic mobile-agent safety.","impact":"Use OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows to bound risk before recurring or unattended execution.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Qiushi Sun; Mukai Li; Zhoumianze Liu; Zhihui Xie; Fangzhi Xu; Zhangyue Yin; Kanzhi Cheng; Zehao Li; Zichen Ding; Qi Liu; Zhiyong Wu; Zhuosheng Zhang; Ben Kao; Lingpeng Kong","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","publisher":"ACL Anthology","doi":"10.18653/v1/2026.acl-long.431","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0361","title":"Effective Context Engineering for AI Agents","url":"https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents","canonical_url":"https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents","annotation":"Anthropic guide to context as managed runtime state rather than a prompt dump.","key_contribution":"Anthropic guide to context as managed runtime state rather than a prompt dump.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Anthropic guide to context as managed runtime state rather than a prompt dump.","impact":"Use Effective Context Engineering for AI Agents to carry context, state, and receipts across runs and failures.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"technical-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0362","title":"Agent Harnesses: the Infrastructure Layer Your LLM Agent Actually Needs","url":"https://ninadpathak.com/blog/agent-harnesses/","canonical_url":"https://ninadpathak.com/blog/agent-harnesses/","annotation":"Covers execution loops, state, checkpointing, observers, and replayability.","key_contribution":"Covers execution loops, state, checkpointing, observers, and replayability.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Covers execution loops, state, checkpointing, observers, and replayability.","impact":"Use Agent Harnesses: the Infrastructure Layer Your LLM Agent Actually Needs to carry context, state, and receipts across runs and failures.","signal":"Contextual source from ninadpathak.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"ninadpathak.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0363","title":"The Agent Loop Is the New OS","url":"https://www.harness.io/blog/agent-loop-new-os","canonical_url":"https://www.harness.io/blog/agent-loop-new-os","annotation":"Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.","key_contribution":"Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.","impact":"Use The Agent Loop Is the New OS to carry context, state, and receipts across runs and failures.","signal":"Contextual source from www.harness.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace;context","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026","publication_year":"2026","publication_venue":"","publisher":"Harness.io","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0364","title":"Harness engineering for coding agent users","url":"https://martinfowler.com/articles/harness-engineering.html","canonical_url":"https://martinfowler.com/articles/harness-engineering.html","annotation":"Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","key_contribution":"Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","novelty":"Makes persistence and context management visible as runtime design choices. Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","impact":"Use Harness engineering for coding agent users to carry context, state, and receipts across runs and failures.","signal":"Contextual source from martinfowler.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"martinfowler.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0365","title":"Context Engineering","url":"https://simonwillison.net/2025/Jun/27/context-engineering/","canonical_url":"https://simonwillison.net/2025/Jun/27/context-engineering/","annotation":"Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","key_contribution":"Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","impact":"Use Context Engineering to carry context, state, and receipts across runs and failures.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"2025","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0366","title":"Agentic Coding in 2026","url":"https://sourcegraph.com/blog/agentic-coding","canonical_url":"https://sourcegraph.com/blog/agentic-coding","annotation":"Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","key_contribution":"Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","impact":"Use Agentic Coding in 2026 to carry context, state, and receipts across runs and failures.","signal":"Contextual source from sourcegraph.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Sourcegraph","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0367","title":"Agentic AI State Management with ScyllaDB and LangGraph","url":"https://www.scylladb.com/2026/04/08/agentic-ai-state-management-with-scylladb-and-langgraph/","canonical_url":"https://www.scylladb.com/2026/04/08/agentic-ai-state-management-with-scylladb-and-langgraph/","annotation":"Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","key_contribution":"Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","impact":"Use Agentic AI State Management with ScyllaDB and LangGraph to carry context, state, and receipts across runs and failures.","signal":"Contextual source from www.scylladb.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Cynthia Dunlop","publication_date":"2026-04-08","publication_year":"2026","publication_venue":"","publisher":"ScyllaDB","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0368","title":"Mem0","url":"https://github.com/mem0ai/mem0","canonical_url":"https://github.com/mem0ai/mem0","annotation":"Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","key_contribution":"Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","novelty":"Persistent memory is treated as an external runtime artifact. Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","impact":"Use Mem0 to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (61,092 stars; 7,110 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-06-20","publication_year":"2023","publication_venue":"mem0ai/mem0","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"mem0ai/mem0","github_stars":"61092","arxiv_id":"","date_added":""},{"row_id":"ale-0369","title":"Letta","url":"https://github.com/letta-ai/letta","canonical_url":"https://github.com/letta-ai/letta","annotation":"Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","key_contribution":"Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","novelty":"Persistent memory is treated as an external runtime artifact. Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","impact":"Use Letta to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (23,838 stars; 2,532 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-10-11","publication_year":"2023","publication_venue":"letta-ai/letta","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"letta-ai/letta","github_stars":"23838","arxiv_id":"","date_added":""},{"row_id":"ale-0370","title":"Zep","url":"https://github.com/getzep/zep","canonical_url":"https://github.com/getzep/zep","annotation":"Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.","key_contribution":"Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.","impact":"Use Zep to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (4,764 stars; 641 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-04-29","publication_year":"2023","publication_venue":"getzep/zep","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"getzep/zep","github_stars":"4764","arxiv_id":"","date_added":""},{"row_id":"ale-0371","title":"LangMem","url":"https://github.com/langchain-ai/langmem","canonical_url":"https://github.com/langchain-ai/langmem","annotation":"SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","key_contribution":"SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","novelty":"Persistent memory is treated as an external runtime artifact. SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","impact":"Use LangMem to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (1,566 stars; 177 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-01-21","publication_year":"2025","publication_venue":"langchain-ai/langmem","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"langchain-ai/langmem","github_stars":"1566","arxiv_id":"","date_added":""},{"row_id":"ale-0372","title":"Beads","url":"https://github.com/steveyegge/beads","canonical_url":"https://github.com/gastownhall/beads","annotation":"Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","key_contribution":"Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","impact":"Use Beads to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (25,405 stars; 1,705 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"intake;context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-10-12","publication_year":"2025","publication_venue":"steveyegge/beads","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"steveyegge/beads","github_stars":"25405","arxiv_id":"","date_added":""},{"row_id":"ale-0373","title":"ARC: Active and Reflection-driven Context Management for Long-Horizon Agents","url":"https://arxiv.org/abs/2601.12030","canonical_url":"https://aclanthology.org/2026.findings-acl.930/","annotation":"Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","key_contribution":"Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","impact":"Use ARC: Active and Reflection-driven Context Management for Long-Horizon Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2601.12030; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yilun Yao; Shan Huang; Elsie Dai; Zhewen Tan; Zhenyu Duan; Shousheng Jia; Yanbing Jiang; Tong Yang","publication_date":"2026","publication_year":"2026","publication_venue":"Findings of the Association for Computational Linguistics: ACL","publisher":"Association for Computational Linguistics","doi":"10.18653/v1/2026.findings-acl.930","publication_note":"Published in Findings of the Association for Computational Linguistics: ACL; the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ACL Anthology and DOI records","github_repo":"","github_stars":"","arxiv_id":"2601.12030","date_added":""},{"row_id":"ale-0374","title":"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers","url":"https://arxiv.org/abs/2603.07670","canonical_url":"https://arxiv.org/abs/2603.07670","annotation":"Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","key_contribution":"Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","impact":"Use Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2603.07670; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Pengfei Du","publication_date":"2026-03-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.07670","date_added":""},{"row_id":"ale-0375","title":"Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering","url":"https://arxiv.org/abs/2604.08224","canonical_url":"https://arxiv.org/abs/2604.08224","annotation":"Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","key_contribution":"Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","impact":"Use Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2604.08224; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Chenyu Zhou; Huacan Chai; Wenteng Chen; Zihan Guo; Rong Shan; Yuanyi Song; Tianyi Xu; Yingxuan Yang; Aofan Yu; Weiming Zhang; Congming Zheng; Jiachen Zhu; Zeyu Zheng; Zhuosheng Zhang; Xingyu Lou; Changwang Zhang; Zhihui Fu; Jun Wang; Weiwen Liu; Jianghao Lin; Weinan Zhang","publication_date":"2026-04-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"54 pages, tech report on Externalization in LLM Agents","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.08224","date_added":""},{"row_id":"ale-0376","title":"Meta Context Engineering via Agentic Skill Evolution","url":"https://arxiv.org/abs/2601.21557","canonical_url":"https://arxiv.org/abs/2601.21557","annotation":"A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","key_contribution":"A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","novelty":"Context is managed as durable loop state rather than a single prompt payload. A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","impact":"Use Meta Context Engineering via Agentic Skill Evolution to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2601.21557; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Haoran Ye; Xuning He; Vincent Arak; Haonan Dong; Guojie Song","publication_date":"2026-01-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"46 pages, 4 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2601.21557","date_added":""},{"row_id":"ale-0377","title":"Are We Ready for an Agent-Native Memory System?","url":"https://arxiv.org/abs/2606.24775","canonical_url":"https://arxiv.org/abs/2606.24775","annotation":"Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","key_contribution":"Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","novelty":"Persistent memory is treated as an external runtime artifact. Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","impact":"Use Are We Ready for an Agent-Native Memory System? to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2606.24775; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Wei Zhou; Xuanhe Zhou; Shaokun Han; Hongming Xu; Guoliang Li; Zhiyu Li; Feiyu Xiong; Fan Wu","publication_date":"2026-06-23","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Paper list available at: https://github.com/OpenDataBox/awesome-agent-memory. Source code available at: https://github.com/OpenDataBox/MemoryData","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.24775","date_added":""},{"row_id":"ale-0378","title":"Self-Evolving World Models for LLM Agent Planning","url":"https://arxiv.org/abs/2606.30639","canonical_url":"https://arxiv.org/abs/2606.30639","annotation":"Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","key_contribution":"Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","novelty":"Makes persistence and context management visible as runtime design choices. Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","impact":"Use Self-Evolving World Models for LLM Agent Planning to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2606.30639; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xuan Zhang; Wenxuan Zhang; See-Kiong Ng; Yang Deng","publication_date":"2026-06-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.30639","date_added":""},{"row_id":"ale-0379","title":"Rethinking Continual Experience Internalization for Self-Evolving LLM Agents","url":"https://arxiv.org/abs/2606.04703","canonical_url":"https://arxiv.org/abs/2606.04703","annotation":"Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","key_contribution":"Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","novelty":"Makes persistence and context management visible as runtime design choices. Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","impact":"Use Rethinking Continual Experience Internalization for Self-Evolving LLM Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2606.04703; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jingwen Chen; Wenkai Yang; Shengda Fan; Wenbo Nie; Chenxing Sun; Shaodong Zheng; Yangen Hu; Lu Pan; Ke Zeng; Yankai Lin","publication_date":"2026-06-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"10 pages, 8 figures","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.04703","date_added":""},{"row_id":"ale-0380","title":"GenericAgent","url":"https://github.com/lsdefine/GenericAgent","canonical_url":"https://github.com/lsdefine/GenericAgent","annotation":"Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","key_contribution":"Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","novelty":"Persistent memory is treated as an external runtime artifact. Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","impact":"Use GenericAgent to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (13,473 stars; 1,559 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"objective;context","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-16","publication_year":"2026","publication_venue":"lsdefine/GenericAgent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"lsdefine/GenericAgent","github_stars":"13473","arxiv_id":"","date_added":""},{"row_id":"ale-0381","title":"Self-GC: Self-Governing Context for Long-Horizon LLM Agents","url":"https://arxiv.org/abs/2607.00692","canonical_url":"https://arxiv.org/abs/2607.00692","annotation":"Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","key_contribution":"Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","impact":"Use Self-GC: Self-Governing Context for Long-Horizon LLM Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.00692; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xubin Hao; Hongjin Meng; Xin Yin; Jiawei Zhu; Chenpeng Cao","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00692","date_added":""},{"row_id":"ale-0382","title":"CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents","url":"https://arxiv.org/abs/2607.05378","canonical_url":"https://arxiv.org/abs/2607.05378","annotation":"Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","key_contribution":"Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","novelty":"Verification is promoted from a final check to a loop-control signal. Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","impact":"Use CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.05378; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yujiang Li; Zhenyu Hou; Yi Jing; Jie Tang; Yuxiao Dong","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05378","date_added":""},{"row_id":"ale-0383","title":"SelfMem: Self-Optimizing Memory for AI Agents","url":"https://arxiv.org/abs/2607.03726","canonical_url":"https://arxiv.org/abs/2607.03726","annotation":"Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","key_contribution":"Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","novelty":"Primary-source operational guidance rather than commentary. Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","impact":"Use SelfMem: Self-Optimizing Memory for AI Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.03726; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Shu Yang; Junchao Wu; Derek F. Wong; Di Wang","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.03726","date_added":""},{"row_id":"ale-0384","title":"Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture","url":"https://arxiv.org/abs/2607.04391","canonical_url":"https://arxiv.org/abs/2607.04391","annotation":"Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","key_contribution":"Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","novelty":"Persistent memory is treated as an external runtime artifact. Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","impact":"Use Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.04391; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Serge Lacasse; Jérémie Hatier; Alex Baker","publication_date":"2026-07-05","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"22 pages, 2 figures","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.04391","date_added":""},{"row_id":"ale-0385","title":"The Log Is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems","url":"https://arxiv.org/abs/2605.21997","canonical_url":"https://arxiv.org/abs/2605.21997","annotation":"BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","key_contribution":"BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","impact":"Use The Log Is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2605.21997; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"objective;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yohei Nakajima","publication_date":"2026-05-21","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"11 pages, 1 figure. Open-source Apache-2.0 implementation with reproducible quickstart demo, deterministic replay, fork-and-diff, and lineage tracing","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.21997","date_added":""},{"row_id":"ale-0386","title":"Agentics: Memorizing Session Transcripts Isn't Useful","url":"https://12gramsofcarbon.com/p/agentics-memorizing-session-transcripts","canonical_url":"https://12gramsofcarbon.com/p/agentics-memorizing-session-transcripts","annotation":"From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","key_contribution":"From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","novelty":"Context is managed as durable loop state rather than a single prompt payload. From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","impact":"Use Agentics: Memorizing Session Transcripts Isn't Useful to carry context, state, and receipts across runs and failures.","signal":"Contextual source from 12gramsofcarbon.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"theahura","publication_date":"","publication_year":"","publication_venue":"","publisher":"12gramsofcarbon.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0387","title":"Long-Running Agents","url":"https://addyo.substack.com/p/long-running-agents","canonical_url":"https://addyo.substack.com/p/long-running-agents","annotation":"Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","key_contribution":"Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","impact":"Use Long-Running Agents to carry context, state, and receipts across runs and failures.","signal":"Contextual source from addyo.substack.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Addy Osmani","publication_date":"","publication_year":"","publication_venue":"","publisher":"Substack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0388","title":"StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems","url":"https://arxiv.org/abs/2607.05844","canonical_url":"https://arxiv.org/abs/2607.05844","annotation":"Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","key_contribution":"Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","novelty":"Persistent memory is treated as an external runtime artifact. Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","impact":"Use StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.05844; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation;state;budget","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Sergey Volkov; Yang Li; Ye Luo","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code and supplementary materials available at: https://github.com/nZiben/statefuse","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05844","date_added":""},{"row_id":"ale-0389","title":"Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents","url":"https://arxiv.org/abs/2607.08716","canonical_url":"https://arxiv.org/abs/2607.08716","annotation":"Names the failure mode \"behavioral state decay\" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","key_contribution":"Names the failure mode \"behavioral state decay\" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","novelty":"Persistent memory is treated as an external runtime artifact. Names the failure mode \"behavioral state decay\" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","impact":"Use Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.08716; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yifan Wu; Lizhu Zhang; Yuhang Zhou; Mingyi Wang; Bo Peng; Serena Li; Xiangjun Fan; Zhuokai Zhao","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08716","date_added":""},{"row_id":"ale-0390","title":"What to Keep, What to Forget: A Rate-Distortion View of Memory Compaction","url":"https://arxiv.org/abs/2607.08032","canonical_url":"https://arxiv.org/abs/2607.08032","annotation":"Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","key_contribution":"Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","novelty":"Persistent memory is treated as an external runtime artifact. Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","impact":"Use What to Keep, What to Forget: A Rate-Distortion View of Memory Compaction to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.08032; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"objective;context;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Ashwin Gerard Colaco; Nada Lahjouji","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08032","date_added":""},{"row_id":"ale-0391","title":"A Hierarchical Memory Architecture Overcomes Context Limits in Long-Horizon Multi-Agent Modeling","url":"https://arxiv.org/abs/2607.07666","canonical_url":"https://arxiv.org/abs/2607.07666","annotation":"Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","key_contribution":"Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","novelty":"Verification is promoted from a final check to a loop-control signal. Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","impact":"Use A Hierarchical Memory Architecture Overcomes Context Limits in Long-Horizon Multi-Agent Modeling to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.07666; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Shivendra G. Tewari; Holly Kimko","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"19 pages, 4 figures, 2 tables. Preprint submitted for publication","primary_category":"q-bio.QM","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07666","date_added":""},{"row_id":"ale-0392","title":"SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents","url":"https://arxiv.org/abs/2607.07676","canonical_url":"https://arxiv.org/abs/2607.07676","annotation":"Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","key_contribution":"Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","novelty":"State persistence is explicit enough for repeated runs and handoff. Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","impact":"Use SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.07676; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Tianming Sha; Yue Zhao; Lichao Sun; Yushun Dong","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"44 pages, 5 figures. Code: https://github.com/LabRAI/SkillCenter ; Data: https://huggingface.co/datasets/Tommysha/skillcenter-bundles","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07676","date_added":""},{"row_id":"ale-0393","title":"How version control will evolve for the agent boom","url":"https://entire.io/blog/how-version-control-will-evolve-for-the-agent-boom","canonical_url":"https://entire.io/blog/how-version-control-will-evolve-for-the-agent-boom","annotation":"Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","key_contribution":"Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","impact":"Use How version control will evolve for the agent boom to carry context, state, and receipts across runs and failures.","signal":"Contextual source from entire.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace;state;exit","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"","publisher":"Entire","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0394","title":"self-learning-skills","url":"https://github.com/Kulaxyz/self-learning-skills","canonical_url":"https://github.com/Kulaxyz/self-learning-skills","annotation":"Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","key_contribution":"Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","impact":"Use self-learning-skills to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (888 stars; 37 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-28","publication_year":"2026","publication_venue":"Kulaxyz/self-learning-skills","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Kulaxyz/self-learning-skills","github_stars":"888","arxiv_id":"","date_added":""},{"row_id":"ale-0395","title":"GitLake: Git-for-data for the agentic lakehouse","url":"https://arxiv.org/abs/2607.08319","canonical_url":"https://arxiv.org/abs/2607.08319","annotation":"Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","key_contribution":"Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","novelty":"Makes persistence and context management visible as runtime design choices. Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","impact":"Use GitLake: Git-for-data for the agentic lakehouse to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.08319; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Weiming Sheng; Jinlang Wang; Manuel Barros; Aldrin Montana; Jacopo Tagliabue; Luca Bigon","publication_date":"2026","publication_year":"2026","publication_venue":"DASHSys Workshop at the International Conference on Very Large Data Bases (VLDB)","publisher":"VLDB Endowment","doi":"","publication_note":"Accepted at DASHSys Workshop at the International Conference on Very Large Data Bases (VLDB); the linked arXiv record is the available paper version.","primary_category":"cs.DB","metadata_source":"Current arXiv acceptance note and official workshop page","github_repo":"","github_stars":"","arxiv_id":"2607.08319","date_added":""},{"row_id":"ale-0396","title":"Shared Selective Persistent Memory for Agentic LLM Systems","url":"https://arxiv.org/abs/2607.09493","canonical_url":"https://arxiv.org/abs/2607.09493","annotation":"Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","key_contribution":"Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","novelty":"Persistent memory is treated as an external runtime artifact. Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","impact":"Use Shared Selective Persistent Memory for Agentic LLM Systems to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.09493; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace;context;state;budget;exit","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Sanjana Pedada; Aditya Dhavala; Neelraj Patil","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"11 pages, 2 figures, 4 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09493","date_added":""},{"row_id":"ale-0397","title":"Scoped Verification for Reliable Long-Horizon Agentic Context Evolution","url":"https://arxiv.org/abs/2607.09175","canonical_url":"https://arxiv.org/abs/2607.09175","annotation":"GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","key_contribution":"GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","impact":"Use Scoped Verification for Reliable Long-Horizon Agentic Context Evolution to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.09175; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Dan C. Hsu; Luke Lu","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"18 pages, 3 figs","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09175","date_added":""},{"row_id":"ale-0398","title":"AgentMemory","url":"https://github.com/rohitg00/agentmemory","canonical_url":"https://github.com/rohitg00/agentmemory","annotation":"Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","key_contribution":"Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","novelty":"Persistent memory is treated as an external runtime artifact. Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","impact":"Use AgentMemory to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (25,307 stars; 2,098 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace;context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-02-25","publication_year":"2026","publication_venue":"rohitg00/agentmemory","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"rohitg00/agentmemory","github_stars":"25307","arxiv_id":"","date_added":""},{"row_id":"ale-0399","title":"TencentDB-Agent-Memory","url":"https://github.com/TencentCloud/TencentDB-Agent-Memory","canonical_url":"https://github.com/TencentCloud/TencentDB-Agent-Memory","annotation":"Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.","key_contribution":"Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.","novelty":"Persistent memory is treated as an external runtime artifact. Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.","impact":"Use TencentDB-Agent-Memory to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (9,082 stars; 842 forks; NOASSERTION license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-07","publication_year":"2026","publication_venue":"TencentCloud/TencentDB-Agent-Memory","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"TencentCloud/TencentDB-Agent-Memory","github_stars":"9082","arxiv_id":"","date_added":""},{"row_id":"ale-0400","title":"agent-memory (Neo4j Labs)","url":"https://github.com/neo4j-labs/agent-memory","canonical_url":"https://github.com/neo4j-labs/agent-memory","annotation":"Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","key_contribution":"Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","novelty":"Primary-source operational guidance rather than commentary. Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","impact":"Use agent-memory (Neo4j Labs) to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (375 stars; 86 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-06","publication_year":"2026","publication_venue":"neo4j-labs/agent-memory","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"neo4j-labs/agent-memory","github_stars":"375","arxiv_id":"","date_added":""},{"row_id":"ale-0401","title":"re_gent","url":"https://github.com/regent-vcs/re_gent","canonical_url":"https://github.com/regent-vcs/re_gent","annotation":"Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","key_contribution":"Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","novelty":"Makes persistence and context management visible as runtime design choices. Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","impact":"Use re_gent to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (780 stars; 57 forks; Apache-2.0 license; updated 2026-07-15); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-04-30","publication_year":"2026","publication_venue":"regent-vcs/re_gent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"regent-vcs/re_gent","github_stars":"780","arxiv_id":"","date_added":""},{"row_id":"ale-0402","title":"StructAgent: Harness Long-Horizon Digital Agents with Unified Causal Structure","url":"https://arxiv.org/abs/2607.11388","canonical_url":"https://arxiv.org/abs/2607.11388","annotation":"State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","key_contribution":"State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","impact":"Use StructAgent: Harness Long-Horizon Digital Agents with Unified Causal Structure to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.11388; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"verification;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Wenyi Wu; Sibo Zhu; Kun Zhou; Aayush Salvi; Zixuan Song; Biwei Huang","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11388","date_added":"2026-07-15"},{"row_id":"ale-0403","title":"The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory","url":"https://arxiv.org/abs/2607.10608","canonical_url":"https://arxiv.org/abs/2607.10608","annotation":"Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","key_contribution":"Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","novelty":"Persistent memory is treated as an external runtime artifact. Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","impact":"Use The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.10608; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yixiong Chen; Xinyi Bai; Alan Yuille","publication_date":"2026-07-12","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.10608","date_added":"2026-07-15"},{"row_id":"ale-0404","title":"Conversational Context: Session, State, and Memory","url":"https://adk.dev/sessions/","canonical_url":"https://adk.dev/sessions/","annotation":"Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","key_contribution":"Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","novelty":"Primary-source operational guidance rather than commentary. Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","impact":"Use Conversational Context: Session, State, and Memory to carry context, state, and receipts across runs and failures.","signal":"Primary official documentation from adk.dev; use it for current product or standard behavior.","resource_type":"Docs","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Google Agent Development Kit","publication_date":"","publication_year":"","publication_venue":"Google Agent Development Kit","publisher":"Google","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0405","title":"Persistence","url":"https://docs.langchain.com/oss/python/langgraph/persistence","canonical_url":"https://docs.langchain.com/oss/python/langgraph/persistence","annotation":"Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","key_contribution":"Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","novelty":"Primary-source operational guidance rather than commentary. Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","impact":"Use Persistence to carry context, state, and receipts across runs and failures.","signal":"Primary official documentation from docs.langchain.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state;escalation","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"LangChain","publication_date":"","publication_year":"","publication_venue":"LangGraph","publisher":"LangChain","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0406","title":"Workflow checkpoints","url":"https://learn.microsoft.com/en-us/agent-framework/workflows/checkpoints","canonical_url":"https://learn.microsoft.com/en-us/agent-framework/workflows/checkpoints","annotation":"Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","key_contribution":"Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","novelty":"Primary-source operational guidance rather than commentary. Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","impact":"Use Workflow checkpoints to carry context, state, and receipts across runs and failures.","signal":"Primary official documentation from learn.microsoft.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Microsoft","publication_date":"","publication_year":"","publication_venue":"Microsoft Agent Framework","publisher":"Microsoft","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0407","title":"Agent state","url":"https://strandsagents.com/docs/user-guide/concepts/agents/state/","canonical_url":"https://strandsagents.com/docs/user-guide/concepts/agents/state/","annotation":"Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","key_contribution":"Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","novelty":"Primary-source operational guidance rather than commentary. Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","impact":"Use Agent state to carry context, state, and receipts across runs and failures.","signal":"Primary official documentation from strandsagents.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"builder","loop_layer":"harness","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Strands Agents","publication_date":"","publication_year":"","publication_venue":"Strands Agents","publisher":"Strands Agents","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0408","title":"Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents","url":"https://arxiv.org/abs/2607.13591","canonical_url":"https://arxiv.org/abs/2607.13591","annotation":"Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","key_contribution":"Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","novelty":"The work turns loop quality into a measurable task or score. Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","impact":"Use Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.13591; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification;budget","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Eric Hanchen Jiang; Zhi Zhang; Yuchen Wu; Levina Li; Dong Liu; Xiao Liang; Rui Sun; Yubei Li; Edward Sun; Haozheng Luo; Zhaolu Kang; Aylin Caliskan; Kai-Wei Chang; Ying Nian Wu","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13591","date_added":"2026-07-17"},{"row_id":"ale-0409","title":"Why Git Is the Memory Solution for the Agentic Development Lifecycle","url":"https://arxiv.org/abs/2607.14390","canonical_url":"https://arxiv.org/abs/2607.14390","annotation":"Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","key_contribution":"Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","novelty":"Persistent memory is treated as an external runtime artifact. Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","impact":"Use Why Git Is the Memory Solution for the Agentic Development Lifecycle to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.14390; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Frank Guo","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"8 pages","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14390","date_added":"2026-07-17"},{"row_id":"ale-0410","title":"ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory","url":"https://arxiv.org/abs/2509.25140","canonical_url":"https://openreview.net/forum?id=jL7fwchScm","annotation":"Distills successful and failed trajectories into reusable reasoning memories, then uses memory-aware test-time scaling to turn additional exploration into better guidance for future runs.","key_contribution":"Distills successful and failed trajectories into reusable reasoning memories, then uses memory-aware test-time scaling to turn additional exploration into better guidance for future runs.","novelty":"Persistent memory is treated as an external runtime artifact. Distills successful and failed trajectories into reusable reasoning memories, then uses memory-aware test-time scaling to turn additional exploration into better guidance for future runs.","impact":"Use ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2509.25140; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Siru Ouyang; Jun Yan; I-Hung Hsu; Yanfei Chen; Ke Jiang; Zifeng Wang; Rujun Han; Long T. Le; Samira Daruki; Xiangru Tang; Vishy Tirumalashetty; George Lee; Mahsan Rofouei; Hangfei Lin; Jiawei Han; Chen-Yu Lee; Tomas Pfister","publication_date":"2026","publication_year":"2026","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"OpenReview proceedings record","github_repo":"","github_stars":"","arxiv_id":"2509.25140","date_added":"2026-07-18"},{"row_id":"ale-0411","title":"Scaling Long-Horizon LLM Agent via Context-Folding","url":"https://arxiv.org/abs/2510.11967","canonical_url":"https://arxiv.org/abs/2510.11967","annotation":"Lets an agent branch into temporary sub-trajectories and fold completed work into compact continuation state, pairing the mechanism with FoldGRPO to operate under a 32K active-context budget.","key_contribution":"Lets an agent branch into temporary sub-trajectories and fold completed work into compact continuation state, pairing the mechanism with FoldGRPO to operate under a 32K active-context budget.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Lets an agent branch into temporary sub-trajectories and fold completed work into compact continuation state, pairing the mechanism with FoldGRPO to operate under a 32K active-context budget.","impact":"Use Scaling Long-Horizon LLM Agent via Context-Folding to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2510.11967; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state;budget","audience":"researcher;evaluator","loop_layer":"harness","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Weiwei Sun; Miao Lu; Zhan Ling; Kang Liu; Xuesong Yao; Yiming Yang; Jiecao Chen","publication_date":"2025-10-13","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2510.11967","date_added":"2026-07-18"},{"row_id":"ale-0412","title":"AutoGen","url":"https://github.com/microsoft/autogen","canonical_url":"https://github.com/microsoft/autogen","annotation":"Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","key_contribution":"Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","impact":"Use AutoGen to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (59,802 stars; 9,000 forks; CC-BY-4.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;delegation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-08-18","publication_year":"2023","publication_venue":"microsoft/autogen","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"microsoft/autogen","github_stars":"59802","arxiv_id":"","date_added":""},{"row_id":"ale-0413","title":"Microsoft Agent Framework","url":"https://github.com/microsoft/agent-framework","canonical_url":"https://github.com/microsoft/agent-framework","annotation":"Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.","key_contribution":"Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.","impact":"Use Microsoft Agent Framework to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (12,198 stars; 2,051 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-04-28","publication_year":"2025","publication_venue":"microsoft/agent-framework","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"microsoft/agent-framework","github_stars":"12198","arxiv_id":"","date_added":""},{"row_id":"ale-0414","title":"LangGraph","url":"https://github.com/langchain-ai/langgraph","canonical_url":"https://github.com/langchain-ai/langgraph","annotation":"Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","key_contribution":"Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","impact":"Use LangGraph to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (37,537 stars; 6,289 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"state;escalation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-08-09","publication_year":"2023","publication_venue":"langchain-ai/langgraph","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"langchain-ai/langgraph","github_stars":"37537","arxiv_id":"","date_added":""},{"row_id":"ale-0415","title":"CrewAI","url":"https://github.com/crewAIInc/crewAI","canonical_url":"https://github.com/crewAIInc/crewAI","annotation":"Framework for multi-agent workflows organized around roles, tasks, and crews.","key_contribution":"Framework for multi-agent workflows organized around roles, tasks, and crews.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. 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API protocol for agent interaction, useful for separating loop managers from agent runtimes.","impact":"Use Agent Protocol to choose an implementation surface for repeatable agent work.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;state","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"technical-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"AgentProtocol.ai","publication_date":"","publication_year":"","publication_venue":"","publisher":"AgentProtocol.ai","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0419","title":"AgentKit","url":"https://github.com/inngest/agent-kit","canonical_url":"https://github.com/inngest/agent-kit","annotation":"TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","key_contribution":"TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","impact":"Use AgentKit to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (913 stars; 136 forks; Apache-2.0 license; updated 2026-07-13); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2024-11-18","publication_year":"2024","publication_venue":"inngest/agent-kit","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"inngest/agent-kit","github_stars":"913","arxiv_id":"","date_added":""},{"row_id":"ale-0420","title":"deepagents","url":"https://github.com/langchain-ai/deepagents","canonical_url":"https://github.com/langchain-ai/deepagents","annotation":"LangChain project for deeper, longer-running agents with middleware and harness patterns.","key_contribution":"LangChain project for deeper, longer-running agents with middleware and harness patterns.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. LangChain project for deeper, longer-running agents with middleware and harness patterns.","impact":"Use deepagents to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (26,393 stars; 3,700 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;state","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-07-27","publication_year":"2025","publication_venue":"langchain-ai/deepagents","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"langchain-ai/deepagents","github_stars":"26393","arxiv_id":"","date_added":""},{"row_id":"ale-0421","title":"Temporal for AI","url":"https://temporal.io/solutions/ai","canonical_url":"https://temporal.io/solutions/ai","annotation":"Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","key_contribution":"Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","impact":"Use Temporal for AI to choose an implementation surface for repeatable agent work.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"state;budget;escalation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"technical-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"temporal.io","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0422","title":"Restate","url":"https://restate.dev/","canonical_url":"https://www.restate.dev/","annotation":"Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","key_contribution":"Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","impact":"Use Restate to choose an implementation surface for repeatable agent work.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"state","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"implementation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Restate","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0423","title":"DBOS","url":"https://www.dbos.dev/","canonical_url":"https://www.dbos.dev/","annotation":"Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","key_contribution":"Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","impact":"Use DBOS to choose an implementation surface for repeatable agent work.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;intake","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"implementation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"dbos.dev","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0424","title":"Composio Agent Orchestrator","url":"https://github.com/ComposioHQ/agent-orchestrator","canonical_url":"https://github.com/AgentWrapper/agent-orchestrator","annotation":"Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","key_contribution":"Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","impact":"Use Composio Agent Orchestrator to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (8,343 stars; 1,204 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;delegation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-02-13","publication_year":"2026","publication_venue":"ComposioHQ/agent-orchestrator","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ComposioHQ/agent-orchestrator","github_stars":"8343","arxiv_id":"","date_added":""},{"row_id":"ale-0425","title":"Omnigent","url":"https://github.com/omnigent-ai/omnigent","canonical_url":"https://github.com/omnigent-ai/omnigent","annotation":"Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","key_contribution":"Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","impact":"Use Omnigent to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (7,440 stars; 1,065 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"budget;escalation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-11","publication_year":"2026","publication_venue":"omnigent-ai/omnigent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"omnigent-ai/omnigent","github_stars":"7440","arxiv_id":"","date_added":""},{"row_id":"ale-0426","title":"From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution","url":"https://arxiv.org/abs/2604.11378","canonical_url":"https://arxiv.org/abs/2604.11378","annotation":"Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","key_contribution":"Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","impact":"Use From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2604.11378; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;escalation;exit","audience":"researcher;evaluator","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Hu Wei","publication_date":"2026-04-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"51 pages, 4 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.11378","date_added":""},{"row_id":"ale-0427","title":"Eve","url":"https://github.com/vercel/eve","canonical_url":"https://github.com/vercel/eve","annotation":"Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","key_contribution":"Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","impact":"Use Eve to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (3,835 stars; 352 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;state","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-16","publication_year":"2026","publication_venue":"vercel/eve","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"vercel/eve","github_stars":"3835","arxiv_id":"","date_added":""},{"row_id":"ale-0428","title":"Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework","url":"https://arxiv.org/abs/2603.11445","canonical_url":"https://openreview.net/forum?id=WUmz4LUbvU","annotation":"Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","key_contribution":"Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","impact":"Use Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2603.11445; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;verification;exit","audience":"researcher;evaluator","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xing Zhang; Yanwei Cui; Guanghui Wang; Wei Qiu; Ziyuan Li; Fangwei Han; Yajing Huang; Hengzhi Qiu; Bing Zhu; Peiyang He","publication_date":"2026","publication_year":"2026","publication_venue":"ICLR Workshop on Multi-Agent Learning: Generalization and Adaptation in Intelligence (MALGAI)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in ICLR Workshop on Multi-Agent Learning: Generalization and Adaptation in Intelligence (MALGAI); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ICLR workshop OpenReview record","github_repo":"","github_stars":"","arxiv_id":"2603.11445","date_added":""},{"row_id":"ale-0429","title":"From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents","url":"https://arxiv.org/abs/2603.22386","canonical_url":"https://arxiv.org/abs/2603.22386","annotation":"Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","key_contribution":"Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","impact":"Use From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2603.22386; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Ling Yue; Kushal Raj Bhandari; Ching-Yun Ko; Dhaval Patel; Shuxin Lin; Nianjun Zhou; Jianxi Gao; Pin-Yu Chen; Shaowu Pan","publication_date":"2026-03-23","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.22386","date_added":""},{"row_id":"ale-0430","title":"Agent-as-a-Router","url":"https://github.com/LanceZPF/agent-as-a-router","canonical_url":"https://github.com/LanceZPF/agent-as-a-router","annotation":"Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","key_contribution":"Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","novelty":"Verification is promoted from a final check to a loop-control signal. Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","impact":"Use Agent-as-a-Router to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (685 stars; 15 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"context;delegation;verification","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-20","publication_year":"2026","publication_venue":"LanceZPF/agent-as-a-router","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"LanceZPF/agent-as-a-router","github_stars":"685","arxiv_id":"","date_added":""},{"row_id":"ale-0431","title":"Amp: Custom Agents","url":"https://ampcode.com/news/custom-agents","canonical_url":"https://ampcode.com/news/custom-agents","annotation":"Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","key_contribution":"Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","impact":"Use Amp: Custom Agents to choose an implementation surface for repeatable agent work.","signal":"Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;delegation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"ampcode.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0432","title":"AgentsMesh","url":"https://github.com/AgentsMesh/AgentsMesh","canonical_url":"https://github.com/AgentsMesh/AgentsMesh","annotation":"Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","key_contribution":"Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","impact":"Use AgentsMesh to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (2,282 stars; 228 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;workspace","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-02-28","publication_year":"2026","publication_venue":"AgentsMesh/AgentsMesh","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"AgentsMesh/AgentsMesh","github_stars":"2282","arxiv_id":"","date_added":""},{"row_id":"ale-0433","title":"Bernstein","url":"https://github.com/sipyourdrink-ltd/bernstein","canonical_url":"https://github.com/sipyourdrink-ltd/bernstein","annotation":"Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","key_contribution":"Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","impact":"Use Bernstein to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (690 stars; 64 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;workspace;delegation;verification","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-22","publication_year":"2026","publication_venue":"sipyourdrink-ltd/bernstein","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"sipyourdrink-ltd/bernstein","github_stars":"690","arxiv_id":"","date_added":""},{"row_id":"ale-0434","title":"Aeon","url":"https://github.com/aaronjmars/aeon","canonical_url":"https://github.com/aeonfun/aeon","annotation":"Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","key_contribution":"Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","novelty":"Persistent memory is treated as an external runtime artifact. Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","impact":"Use Aeon to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (578 stars; 208 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;context;state","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-04","publication_year":"2026","publication_venue":"aaronjmars/aeon","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"aaronjmars/aeon","github_stars":"578","arxiv_id":"","date_added":""},{"row_id":"ale-0435","title":"h5i","url":"https://github.com/h5i-dev/h5i","canonical_url":"https://github.com/h5i-dev/h5i","annotation":"Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","key_contribution":"Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","impact":"Use h5i to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (470 stars; 39 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;verification","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-11","publication_year":"2026","publication_venue":"h5i-dev/h5i","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"h5i-dev/h5i","github_stars":"470","arxiv_id":"","date_added":""},{"row_id":"ale-0436","title":"SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery","url":"https://arxiv.org/abs/2607.02807","canonical_url":"https://arxiv.org/abs/2607.02807","annotation":"A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","key_contribution":"A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","novelty":"Context is managed as durable loop state rather than a single prompt payload. A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","impact":"Use SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.02807; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"intake;context","audience":"researcher;evaluator","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yuvraj Virk; Zack Edds; Chunqiu Steven Xia; Lingming Zhang","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02807","date_added":""},{"row_id":"ale-0437","title":"Scaling Long-Running Autonomous Coding","url":"https://cursor.com/blog/scaling-agents","canonical_url":"https://cursor.com/blog/scaling-agents","annotation":"Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","key_contribution":"Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","impact":"Use Scaling Long-Running Autonomous Coding to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;state","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Wilson Lin","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0438","title":"babysitter","url":"https://github.com/a5c-ai/babysitter","canonical_url":"https://github.com/a5c-ai/babysitter","annotation":"Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","key_contribution":"Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","impact":"Use babysitter to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,559 stars; 91 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;verification;state;escalation;exit","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-05","publication_year":"2026","publication_venue":"a5c-ai/babysitter","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"a5c-ai/babysitter","github_stars":"1559","arxiv_id":"","date_added":""},{"row_id":"ale-0439","title":"claude-code-merge-queue","url":"https://github.com/funador/claude-code-merge-queue","canonical_url":"https://github.com/funador/claude-code-merge-queue","annotation":"Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","key_contribution":"Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","impact":"Use claude-code-merge-queue to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (12 stars; 1 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"intake","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"funador/claude-code-merge-queue","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"funador/claude-code-merge-queue","github_stars":"12","arxiv_id":"","date_added":""},{"row_id":"ale-0440","title":"Devin can now manage Devins","url":"https://cognition.com/blog/devin-can-now-manage-devins","canonical_url":"https://cognition.com/blog/devin-can-now-manage-devins","annotation":"Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","key_contribution":"Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","impact":"Use Devin can now manage Devins to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-03-19","publication_year":"2026","publication_venue":"","publisher":"cognition.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0441","title":"pilotfish","url":"https://github.com/Nanako0129/pilotfish","canonical_url":"https://github.com/Nanako0129/pilotfish","annotation":"Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","key_contribution":"Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","novelty":"Verification is promoted from a final check to a loop-control signal. Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","impact":"Use pilotfish to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (480 stars; 37 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"context;delegation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"Nanako0129/pilotfish","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Nanako0129/pilotfish","github_stars":"480","arxiv_id":"","date_added":""},{"row_id":"ale-0442","title":"fable-advisor","url":"https://github.com/DannyMac180/fable-advisor","canonical_url":"https://github.com/DannyMac180/fable-advisor","annotation":"Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","key_contribution":"Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","novelty":"Verification is promoted from a final check to a loop-control signal. Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","impact":"Use fable-advisor to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (529 stars; 45 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"verification","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-03","publication_year":"2026","publication_venue":"DannyMac180/fable-advisor","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"DannyMac180/fable-advisor","github_stars":"529","arxiv_id":"","date_added":""},{"row_id":"ale-0443","title":"agent-chief","url":"https://github.com/SmileLikeYe/agent-chief","canonical_url":"https://github.com/SmileLikeYe/agent-chief","annotation":"Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","key_contribution":"Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","impact":"Use agent-chief to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (867 stars; 3 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"escalation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"SmileLikeYe/agent-chief","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"SmileLikeYe/agent-chief","github_stars":"867","arxiv_id":"","date_added":""},{"row_id":"ale-0444","title":"OpenTag","url":"https://github.com/amplifthq/opentag","canonical_url":"https://github.com/amplifthq/opentag","annotation":"Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","key_contribution":"Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","impact":"Use OpenTag to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,363 stars; 73 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"context","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"amplifthq/opentag","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"amplifthq/opentag","github_stars":"1363","arxiv_id":"","date_added":""},{"row_id":"ale-0445","title":"herdr","url":"https://github.com/ogulcancelik/herdr","canonical_url":"https://github.com/ogulcancelik/herdr","annotation":"Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","key_contribution":"Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","novelty":"State persistence is explicit enough for repeated runs and handoff. Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","impact":"Use herdr to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (17,770 stars; 1,121 forks; NOASSERTION license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"state","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-27","publication_year":"2026","publication_venue":"ogulcancelik/herdr","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ogulcancelik/herdr","github_stars":"17770","arxiv_id":"","date_added":""},{"row_id":"ale-0446","title":"Orca","url":"https://github.com/stablyai/orca","canonical_url":"https://github.com/stablyai/orca","annotation":"Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","key_contribution":"Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","novelty":"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","impact":"Use Orca to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (21,352 stars; 1,539 forks; MIT license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-17","publication_year":"2026","publication_venue":"stablyai/orca","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"stablyai/orca","github_stars":"21352","arxiv_id":"","date_added":""},{"row_id":"ale-0447","title":"Agentic Routing: The Harness-Native Data Flywheel","url":"https://arxiv.org/abs/2607.11399","canonical_url":"https://arxiv.org/abs/2607.11399","annotation":"Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","key_contribution":"Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","novelty":"State persistence is explicit enough for repeated runs and handoff. Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","impact":"Use Agentic Routing: The Harness-Native Data Flywheel to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.11399; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"state;budget","audience":"researcher;evaluator","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xinchen Liu; Hang Zhou; Yingjie Zong; Yuchuan Tian; Liuyang Song; Shuo Zhang; Yulong Li; Wei He; Mengyu Zheng; Runke Liu; Siyang Cheng; Xiang Kuang; Hailin Hu; Kai Han; Yunhe Wang","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code: https://github.com/opensquilla/opensquilla","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11399","date_added":"2026-07-15"},{"row_id":"ale-0448","title":"A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution","url":"https://arxiv.org/abs/2607.11138","canonical_url":"https://arxiv.org/abs/2607.11138","annotation":"Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","key_contribution":"Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","impact":"Use A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2607.11138; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"intake;delegation","audience":"researcher;evaluator","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Prashant Devadiga; Abhishek; Adithya Mishra; Alok Singh; Amisha Sinha; Asit Desai; Gaurang Dahad; Harshit Bhushan; Mandati Pramod Reddy; Prakhar Gupta; Rupesh Patil; Siddhi Behere","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11138","date_added":"2026-07-15"},{"row_id":"ale-0449","title":"Graph-based agent workflows","url":"https://adk.dev/graphs/","canonical_url":"https://adk.dev/graphs/","annotation":"Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","key_contribution":"Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","novelty":"Primary-source operational guidance rather than commentary. Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","impact":"Use Graph-based agent workflows to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from adk.dev; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;state;escalation","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Google Agent Development Kit","publication_date":"","publication_year":"","publication_venue":"Google Agent Development Kit","publisher":"Google","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0450","title":"Flows","url":"https://docs.crewai.com/en/concepts/flows","canonical_url":"https://docs.crewai.com/v1.15.4/en/concepts/flows","annotation":"Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","key_contribution":"Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","novelty":"Primary-source operational guidance rather than commentary. Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","impact":"Use Flows to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from docs.crewai.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;state","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"CrewAI","publication_date":"","publication_year":"","publication_venue":"CrewAI","publisher":"CrewAI","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0451","title":"Graph","url":"https://strandsagents.com/docs/user-guide/concepts/multi-agent/graph/","canonical_url":"https://strandsagents.com/docs/user-guide/concepts/multi-agent/graph/","annotation":"Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","key_contribution":"Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","novelty":"Primary-source operational guidance rather than commentary. Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","impact":"Use Graph to choose an implementation surface for repeatable agent work.","signal":"Primary official documentation from strandsagents.com; use it for current product or standard behavior.","resource_type":"Docs","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;state","audience":"builder","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"official-documentation","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Strands Agents","publication_date":"","publication_year":"","publication_venue":"Strands Agents","publisher":"Strands Agents","doi":"","publication_note":"","primary_category":"","metadata_source":"primary-page","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-17"},{"row_id":"ale-0452","title":"Towards a Science of Scaling Agent Systems","url":"https://arxiv.org/abs/2512.08296","canonical_url":"https://arxiv.org/abs/2512.08296","annotation":"Controls 260 configurations across six agentic benchmarks to show how coordination, model capability, task parallelism, and tool load shape performance and error amplification.","key_contribution":"Controls 260 configurations across six agentic benchmarks to show how coordination, model capability, task parallelism, and tool load shape performance and error amplification.","novelty":"The work turns loop quality into a measurable task or score. Controls 260 configurations across six agentic benchmarks to show how coordination, model capability, task parallelism, and tool load shape performance and error amplification.","impact":"Use Towards a Science of Scaling Agent Systems to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2512.08296; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"workflow","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yubin Kim; Ken Gu; Chanwoo Park; Chunjong Park; Samuel Schmidgall; A. Ali Heydari; Yao Yan; Zhihan Zhang; Yuchen Zhuang; Yun Liu; Mark Malhotra; Paul Pu Liang; Hae Won Park; Yuzhe Yang; Xuhai Xu; Yilun Du; Shwetak Patel; Tim Althoff; Daniel McDuff; Xin Liu","publication_date":"2025-12-09","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2512.08296","date_added":"2026-07-18"},{"row_id":"ale-0453","title":"SWE-bench","url":"https://www.swebench.com/","canonical_url":"https://www.swebench.com/","annotation":"Benchmark for resolving real GitHub issues through code editing and tests.","key_contribution":"Benchmark for resolving real GitHub issues through code editing and tests.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for resolving real GitHub issues through code editing and tests.","impact":"Use SWE-bench to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"swebench.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0454","title":"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?","url":"https://arxiv.org/abs/2310.06770","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/edac78c3e300629acfe6cbe9ca88fb84-Abstract-Conference.html","annotation":"Original SWE-bench paper.","key_contribution":"Original SWE-bench paper.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Original SWE-bench paper.","impact":"Use SWE-bench: Can Language Models Resolve Real-World GitHub Issues? to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2310.06770; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"intake","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Carlos E. Jimenez; John Yang; Alexander Wettig; Shunyu Yao; Kexin Pei; Ofir Press; Karthik Narasimhan","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.CL","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2310.06770","date_added":""},{"row_id":"ale-0455","title":"SWE-bench Goes Live","url":"https://arxiv.org/abs/2505.23419","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2025/hash/d83c4a745789690f82e86d0ef752ae7c-Abstract-Datasets_and_Benchmarks_Track.html","annotation":"Dynamic benchmark designed to reduce overfitting to static issue sets.","key_contribution":"Dynamic benchmark designed to reduce overfitting to static issue sets.","novelty":"The work turns loop quality into a measurable task or score. Dynamic benchmark designed to reduce overfitting to static issue sets.","impact":"Use SWE-bench Goes Live to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2505.23419; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Linghao Zhang; Shilin He; Chaoyun Zhang; Yu Kang; Bowen Li; Chengxing Xie; Junhao Wang; Maoquan Wang; Yufan Huang; Shengyu Fu; Elsie Nallipogu; Qingwei Lin; Yingnong Dang; Saravan Rajmohan; Dongmei Zhang","publication_date":"2025","publication_year":"2025","publication_venue":"Advances in Neural Information Processing Systems 38: Datasets and Benchmarks Track (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"","publication_note":"Published in Advances in Neural Information Processing Systems 38: Datasets and Benchmarks Track (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"NeurIPS proceedings record","github_repo":"","github_stars":"","arxiv_id":"2505.23419","date_added":""},{"row_id":"ale-0456","title":"Terminal-Bench","url":"https://www.tbench.ai/","canonical_url":"https://www.tbench.ai/","annotation":"Benchmark for agents operating in terminal environments.","key_contribution":"Benchmark for agents operating in terminal environments.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for agents operating in terminal environments.","impact":"Use Terminal-Bench to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Terminal-Bench","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0457","title":"Terminal-Bench repository","url":"https://github.com/harbor-framework/terminal-bench","canonical_url":"https://github.com/harbor-framework/terminal-bench","annotation":"Open-source benchmark and harness for hard terminal tasks.","key_contribution":"Open-source benchmark and harness for hard terminal tasks.","novelty":"The work turns loop quality into a measurable task or score. Open-source benchmark and harness for hard terminal tasks.","impact":"Use Terminal-Bench repository to measure progress and gate completion with repeatable evidence.","signal":"Inspectable GitHub source (2,461 stars; 558 forks; Apache-2.0 license; updated 2026-07-18); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"builder;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-01-17","publication_year":"2025","publication_venue":"harbor-framework/terminal-bench","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"harbor-framework/terminal-bench","github_stars":"2461","arxiv_id":"","date_added":""},{"row_id":"ale-0458","title":"AgentBench","url":"https://arxiv.org/abs/2308.03688","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/e9df36b21ff4ee211a8b71ee8b7e9f57-Abstract-Conference.html","annotation":"Multi-environment benchmark for evaluating LLMs as agents.","key_contribution":"Multi-environment benchmark for evaluating LLMs as agents.","novelty":"The work turns loop quality into a measurable task or score. Multi-environment benchmark for evaluating LLMs as agents.","impact":"Use AgentBench to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2308.03688; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xiao Liu; Hao Yu; Hanchen Zhang; Yifan Xu; Xuanyu Lei; Hanyu Lai; Yu Gu; Hangliang Ding; Kaiwen Men; Kejuan Yang; Shudan Zhang; Xiang Deng; Aohan Zeng; Zhengxiao Du; Chenhui Zhang; Sheng Shen; Tianjun Zhang; Yu Su; Huan Sun; Minlie Huang; Yuxiao Dong; Jie Tang","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2308.03688","date_added":""},{"row_id":"ale-0459","title":"WebArena","url":"https://arxiv.org/abs/2307.13854","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/4410c0711e9154a7a2d26f9b3816d1ef-Abstract-Conference.html","annotation":"Realistic web environment for autonomous agents.","key_contribution":"Realistic web environment for autonomous agents.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Realistic web environment for autonomous agents.","impact":"Use WebArena to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2307.13854; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Shuyan Zhou; Frank F. Xu; Hao Zhu; Xuhui Zhou; Robert Lo; Abishek Sridhar; Xianyi Cheng; Tianyue Ou; Yonatan Bisk; Daniel Fried; Uri Alon; Graham Neubig","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2307.13854","date_added":""},{"row_id":"ale-0460","title":"OSWorld","url":"https://arxiv.org/abs/2404.07972","canonical_url":"https://proceedings.neurips.cc/paper_files/paper/2024/hash/5d413e48f84dc61244b6be550f1cd8f5-Abstract-Datasets_and_Benchmarks_Track.html","annotation":"Benchmark for multimodal agents operating full computer environments.","key_contribution":"Benchmark for multimodal agents operating full computer environments.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for multimodal agents operating full computer environments.","impact":"Use OSWorld to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2404.07972; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Tianbao Xie; Danyang Zhang; Jixuan Chen; Xiaochuan Li; Siheng Zhao; Ruisheng Cao; Toh Jing Hua; Zhoujun Cheng; Dongchan Shin; Fangyu Lei; Yitao Liu; Yiheng Xu; Shuyan Zhou; Silvio Savarese; Caiming Xiong; Victor Zhong; Tao Yu","publication_date":"2024","publication_year":"2024","publication_venue":"Advances in Neural Information Processing Systems 37: Datasets and Benchmarks Track (NeurIPS)","publisher":"Neural Information Processing Systems Foundation","doi":"10.52202/079017-1650","publication_note":"Published in Advances in Neural Information Processing Systems 37: Datasets and Benchmarks Track (NeurIPS); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"NeurIPS proceedings and DOI records","github_repo":"","github_stars":"","arxiv_id":"2404.07972","date_added":""},{"row_id":"ale-0461","title":"ToolBench","url":"https://arxiv.org/abs/2307.16789","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/28e50ee5b72e90b50e7196fde8ea260e-Abstract-Conference.html","annotation":"Tool-use benchmark and dataset for tool-augmented agents.","key_contribution":"Tool-use benchmark and dataset for tool-augmented agents.","novelty":"Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Tool-use benchmark and dataset for tool-augmented agents.","impact":"Use ToolBench to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2307.16789; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yujia Qin; Shihao Liang; Yining Ye; Kunlun Zhu; Lan Yan; Yaxi Lu; Yankai Lin; Xin Cong; Xiangru Tang; Bill Qian; Sihan Zhao; Lauren Hong; Runchu Tian; Ruobing Xie; Jie Zhou; Mark Gerstein; Dahai Li; Zhiyuan Liu; Maosong Sun","publication_date":"2024","publication_year":"2024","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2307.16789","date_added":""},{"row_id":"ale-0462","title":"GAIA","url":"https://arxiv.org/abs/2311.12983","canonical_url":"https://arxiv.org/abs/2311.12983","annotation":"Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","key_contribution":"Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","impact":"Use GAIA to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2311.12983; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Grégoire Mialon; Clémentine Fourrier; Craig Swift; Thomas Wolf; Yann LeCun; Thomas Scialom","publication_date":"2023-11-21","publication_year":"2023","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2311.12983","date_added":""},{"row_id":"ale-0463","title":"Tau-bench","url":"https://arxiv.org/abs/2406.12045","canonical_url":"https://arxiv.org/abs/2406.12045","annotation":"Benchmark for tool-agent-user interactions in realistic domains.","key_contribution":"Benchmark for tool-agent-user interactions in realistic domains.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for tool-agent-user interactions in realistic domains.","impact":"Use Tau-bench to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2406.12045; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Shunyu Yao; Noah Shinn; Pedram Razavi; Karthik Narasimhan","publication_date":"2024-06-17","publication_year":"2024","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2406.12045","date_added":""},{"row_id":"ale-0464","title":"VisualWebArena","url":"https://arxiv.org/abs/2401.13649","canonical_url":"https://aclanthology.org/2024.acl-long.50/","annotation":"Visually grounded web-agent benchmark extending WebArena.","key_contribution":"Visually grounded web-agent benchmark extending WebArena.","novelty":"The work turns loop quality into a measurable task or score. Visually grounded web-agent benchmark extending WebArena.","impact":"Use VisualWebArena to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2401.13649; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Jing Yu Koh; Robert Lo; Lawrence Jang; Vikram Duvvur; Ming Chong Lim; Po-Yu Huang; Graham Neubig; Shuyan Zhou; Ruslan Salakhutdinov; Daniel Fried","publication_date":"2024","publication_year":"2024","publication_venue":"Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL)","publisher":"Association for Computational Linguistics","doi":"10.18653/v1/2024.acl-long.50","publication_note":"Published in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL); the linked arXiv record remains available for open access.","primary_category":"cs.LG","metadata_source":"ACL Anthology and DOI records","github_repo":"","github_stars":"","arxiv_id":"2401.13649","date_added":""},{"row_id":"ale-0465","title":"AppWorld","url":"https://arxiv.org/abs/2407.18901","canonical_url":"https://aclanthology.org/2024.acl-long.850/","annotation":"Benchmark of interactive app tasks with state-based and execution-based evaluation.","key_contribution":"Benchmark of interactive app tasks with state-based and execution-based evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Benchmark of interactive app tasks with state-based and execution-based evaluation.","impact":"Use AppWorld to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2407.18901; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;state","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Harsh Trivedi; Tushar Khot; Mareike Hartmann; Ruskin Manku; Vinty Dong; Edward Li; Shashank Gupta; Ashish Sabharwal; Niranjan Balasubramanian","publication_date":"2024","publication_year":"2024","publication_venue":"Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL)","publisher":"Association for Computational Linguistics","doi":"10.18653/v1/2024.acl-long.850","publication_note":"Published in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL); the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ACL Anthology and DOI records","github_repo":"","github_stars":"","arxiv_id":"2407.18901","date_added":""},{"row_id":"ale-0466","title":"Vending-Bench","url":"https://arxiv.org/abs/2502.15840","canonical_url":"https://arxiv.org/abs/2502.15840","annotation":"Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.","key_contribution":"Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.","impact":"Use Vending-Bench to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2502.15840; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Axel Backlund; Lukas Petersson","publication_date":"2025-02-20","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2502.15840","date_added":""},{"row_id":"ale-0467","title":"Vending-Bench leaderboard","url":"https://andonlabs.com/evals/vending-bench","canonical_url":"https://andonlabs.com/evals/vending-bench","annotation":"Live long-horizon coherence results from Andon Labs.","key_contribution":"Live long-horizon coherence results from Andon Labs.","novelty":"The work turns loop quality into a measurable task or score. Live long-horizon coherence results from Andon Labs.","impact":"Use Vending-Bench leaderboard to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"andonlabs.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0468","title":"SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios","url":"https://arxiv.org/abs/2512.18470","canonical_url":"https://arxiv.org/abs/2512.18470","annotation":"Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","key_contribution":"Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","novelty":"The work turns loop quality into a measurable task or score. Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","impact":"Use SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Tue Le; Minh V. T. Thai; Dung Nguyen Manh; Huy Phan Nhat; Nghi D. Q. Bui","publication_date":"2025-12-20","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2512.18470","date_added":""},{"row_id":"ale-0469","title":"EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification","url":"https://arxiv.org/abs/2604.01687","canonical_url":"https://arxiv.org/abs/2604.01687","annotation":"A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","key_contribution":"A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","novelty":"Verification is promoted from a final check to a loop-control signal. A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","impact":"Use EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2604.01687; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Hanrong Zhang; Shicheng Fan; Henry Peng Zou; Yankai Chen; Zhenting Wang; Jiayu Zhou; Chengze Li; Wei-Chieh Huang; Yifei Yao; Kening Zheng; Xue Liu; Xiaoxiao Li; Philip S. Yu","publication_date":"2026-04-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code will be released","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.01687","date_added":""},{"row_id":"ale-0470","title":"SaaSBench: Coding Agents in Long-Horizon Enterprise SaaS Engineering","url":"https://arxiv.org/abs/2605.17526","canonical_url":"https://arxiv.org/abs/2605.17526","annotation":"Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","key_contribution":"Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","impact":"Use SaaSBench: Coding Agents in Long-Horizon Enterprise SaaS Engineering to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Qingnan Ren; Shun Zou; Shiting Huang; Ziao Zhang; Kou Shi; Zhen Fang; Yiming Zhao; Yu Zeng; Qisheng Su; Lin Chen; Yong Wang; Zehui Chen; Xiangxiang Chu; Feng Zhao","publication_date":"2026-05-17","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.17526","date_added":""},{"row_id":"ale-0471","title":"RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades","url":"https://arxiv.org/abs/2605.15846","canonical_url":"https://arxiv.org/abs/2605.15846","annotation":"115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","key_contribution":"115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","novelty":"The work targets tasks that exceed a single context window or prompt session. 115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","impact":"Use RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Xinbo Xu; Ruihan Yang; Haiyang Shen; Wendong Xu; Bofei Gao; Ruoyu Wu; Kean Shi; Weichu Xie; Xuanzhong Chen; Ming Wu; Jason Zeng; Michael Heinrich; Elvis Zhang; Liang Chen; Kuan Li; Baobao Chang","publication_date":"2026-05-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"30 pages, 15 figures","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.15846","date_added":""},{"row_id":"ale-0472","title":"RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code","url":"https://arxiv.org/abs/2503.07832","canonical_url":"https://proceedings.iclr.cc/paper_files/paper/2025/hash/6b44ee74539ea77d6a0d50d468724371-Abstract-Conference.html","annotation":"Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","key_contribution":"Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","impact":"Use RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"state","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Dhruv Gautam; Spandan Garg; Jinu Jang; Neel Sundaresan; Roshanak Zilouchian Moghaddam","publication_date":"2025","publication_year":"2025","publication_venue":"International Conference on Learning Representations (ICLR)","publisher":"International Conference on Learning Representations","doi":"","publication_note":"Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access.","primary_category":"cs.AI","metadata_source":"ICLR proceedings record","github_repo":"","github_stars":"","arxiv_id":"2503.07832","date_added":""},{"row_id":"ale-0473","title":"RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents","url":"https://arxiv.org/abs/2606.22678","canonical_url":"https://arxiv.org/abs/2606.22678","annotation":"Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","key_contribution":"Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","novelty":"Verification is promoted from a final check to a loop-control signal. Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","impact":"Use RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Meher Bhaskar Madiraju; Meher Sai Preetam Madiraju","publication_date":"2026-06-21","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"9 pages, 7 tables, 1 figure","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.22678","date_added":""},{"row_id":"ale-0474","title":"SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks","url":"https://arxiv.org/abs/2603.24755","canonical_url":"https://arxiv.org/abs/2603.24755","annotation":"Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","key_contribution":"Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","impact":"Use SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;state;budget","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Gabriel Orlanski; Devjeet Roy; Alexander Yun; Changho Shin; Alex Gu; Albert Ge; Dyah Adila; Nicholas Roberts; Frederic Sala; Aws Albarghouthi","publication_date":"2026-03-25","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"10.5281/zenodo.18405900,","publication_note":"Code and Leaderboards are located at https://www.scbench.ai","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.24755","date_added":""},{"row_id":"ale-0475","title":"LongCLI-Bench: A Preliminary Benchmark for Long-horizon Agentic Programming in Command-Line Interfaces","url":"https://arxiv.org/abs/2602.14337","canonical_url":"https://aclanthology.org/2026.findings-acl.1497/","annotation":"Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","key_contribution":"Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","novelty":"The work turns loop quality into a measurable task or score. Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","impact":"Use LongCLI-Bench: A Preliminary Benchmark for Long-horizon Agentic Programming in Command-Line Interfaces to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Yukang Feng; Jianwen Sun; Zelai Yang; Jiaxin Ai; Chuanhao Li; Zizhen Li; Fanrui Zhang; Kang He; Rui Ma; Jifan Lin; Jie Sun; Yang Xiao; Sizhuo Zhou; Wenxiao Wu; Yiming Liu; Pengfei Liu; Yu Qiao; Shenglin Zhang; Kaipeng Zhang","publication_date":"2026","publication_year":"2026","publication_venue":"Findings of the Association for Computational Linguistics: ACL","publisher":"Association for Computational Linguistics","doi":"10.18653/v1/2026.findings-acl.1497","publication_note":"Published in Findings of the Association for Computational Linguistics: ACL; the linked arXiv record remains available for open access.","primary_category":"cs.SE","metadata_source":"ACL Anthology and DOI records","github_repo":"","github_stars":"","arxiv_id":"2602.14337","date_added":""},{"row_id":"ale-0476","title":"Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?","url":"https://arxiv.org/abs/2606.29920","canonical_url":"https://arxiv.org/abs/2606.29920","annotation":"Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","key_contribution":"Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","impact":"Use Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios? to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Yangda Peng; Yunjia Qi; Hao Peng; Haotian Xia; Guanzhong He; Xintong Shi; Richeng Xuan; Songyuanyi Lu; Yixian Liu; Zhichao Hu; Yuhong Liu; Lei Hou; Bin Xu; Juanzi Li","publication_date":"2026-06-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.29920","date_added":""},{"row_id":"ale-0477","title":"SentinelBench: A Benchmark for Long-Running Monitoring Agents","url":"https://arxiv.org/abs/2606.05342","canonical_url":"https://arxiv.org/abs/2606.05342","annotation":"Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","key_contribution":"Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","novelty":"The work turns loop quality into a measurable task or score. Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","impact":"Use SentinelBench: A Benchmark for Long-Running Monitoring Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Matheus Kunzler Maldaner; Adam Fourney; Amanda Swearngin; Hussein Mozannar; Gagan Bansal; Maya Murad; Rafah Hosn; Saleema Amershi","publication_date":"2026-06-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"18 pages, 16 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.05342","date_added":""},{"row_id":"ale-0478","title":"SWE-Together: Evaluating Coding Agents in Interactive User Sessions","url":"https://arxiv.org/abs/2606.29957","canonical_url":"https://arxiv.org/abs/2606.29957","annotation":"Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","key_contribution":"Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","novelty":"The work turns loop quality into a measurable task or score. Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","impact":"Use SWE-Together: Evaluating Coding Agents in Interactive User Sessions to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Yifan Wu; Zhuokai Zhao; Songlin Li; Ho Hin Lee; Jiacheng Zhu; Shirley Wu; Tianhe Yu; Serena Li; Lizhu Zhang; Xiangjun Fan; Shengzhi Li","publication_date":"2026-06-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.29957","date_added":""},{"row_id":"ale-0479","title":"The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break","url":"https://arxiv.org/abs/2604.11978","canonical_url":"https://arxiv.org/abs/2604.11978","annotation":"Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","key_contribution":"Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","novelty":"The work turns loop quality into a measurable task or score. Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","impact":"Use The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Xinyu Jessica Wang; Haoyue Bai; Yiyou Sun; Haorui Wang; Shuibai Zhang; Wenjie Hu; Mya Schroder; Bilge Mutlu; Dawn Song; Robert D Nowak","publication_date":"2026-04-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.11978","date_added":""},{"row_id":"ale-0480","title":"Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents","url":"https://arxiv.org/abs/2603.29231","canonical_url":"https://arxiv.org/abs/2603.29231","annotation":"Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","key_contribution":"Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","novelty":"The work targets tasks that exceed a single context window or prompt session. Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","impact":"Use Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2603.29231; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Aaditya Khanal; Yangyang Tao; Junxiu Zhou","publication_date":"2026-03-31","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"23 pages, 4 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.29231","date_added":""},{"row_id":"ale-0481","title":"SEAGym: An Evaluation Environment for Self-Evolving LLM Agents","url":"https://arxiv.org/abs/2606.17546","canonical_url":"https://arxiv.org/abs/2606.17546","annotation":"Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","key_contribution":"Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","impact":"Use SEAGym: An Evaluation Environment for Self-Evolving LLM Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;context;verification;budget","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Congjie Zheng; Chuanyi Xue; Bin Liang; Jun Yang; Changshui Zhang","publication_date":"2026-06-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.17546","date_added":""},{"row_id":"ale-0482","title":"EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions","url":"https://arxiv.org/abs/2605.24110","canonical_url":"https://arxiv.org/abs/2605.24110","annotation":"Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","key_contribution":"Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","impact":"Use EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Haiyang Shen; Xuanzhong Chen; Wendong Xu; Yun Ma; Liang Chen; Kuan Li","publication_date":"2026-05-22","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Work in Progress; 32 pages, 10 figures, preprint","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.24110","date_added":""},{"row_id":"ale-0483","title":"On the Reliability of Computer Use Agents","url":"https://arxiv.org/abs/2604.17849","canonical_url":"https://arxiv.org/abs/2604.17849","annotation":"Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","key_contribution":"Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","impact":"Use On the Reliability of Computer Use Agents to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2604.17849; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Gonzalo Gonzalez-Pumariega; Saaket Agashe; Jiachen Yang; Ang Li; Xin Eric Wang","publication_date":"2026-04-20","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"33 pages, 3 figures, 4 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.17849","date_added":""},{"row_id":"ale-0484","title":"AgentLens: Revealing the Lucky Pass Problem in SWE-Agent Evaluation","url":"https://arxiv.org/abs/2605.12925","canonical_url":"https://arxiv.org/abs/2605.12925","annotation":"Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","key_contribution":"Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","impact":"Use AgentLens: Revealing the Lucky Pass Problem in SWE-Agent Evaluation to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2605.12925; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Priyam Sahoo; Gaurav Mittal; Xiaomin Li; Shengjie Ma; Benjamin Steenhoek; Pingping Lin; Yu Hu","publication_date":"2026-05-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.12925","date_added":""},{"row_id":"ale-0485","title":"ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction","url":"https://arxiv.org/abs/2601.21008","canonical_url":"https://openreview.net/pdf/16a0193aa4e71ffe6c921ac0081a66b525eea017.pdf","annotation":"Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","key_contribution":"Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","novelty":"Verification is promoted from a final check to a loop-control signal. Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","impact":"Use ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"trigger;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Ruicheng Ao; David Simchi-Levi; Xinshang Wang","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 43rd International Conference on Machine Learning (ICML), PMLR 306","publisher":"PMLR","doi":"","publication_note":"Published in Proceedings of the 43rd International Conference on Machine Learning (ICML), PMLR 306; the linked arXiv record remains available for open access.","primary_category":"cs.LG","metadata_source":"PMLR camera-ready record","github_repo":"","github_stars":"","arxiv_id":"2601.21008","date_added":""},{"row_id":"ale-0486","title":"LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis","url":"https://arxiv.org/abs/2605.30434","canonical_url":"https://arxiv.org/abs/2605.30434","annotation":"Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","key_contribution":"Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","novelty":"The work turns loop quality into a measurable task or score. Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","impact":"Use LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;state","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Kewei Xu; Xiaoben Lu; Shuofei Qiao; Zihan Ding; Haoming Xu; Lei Liang; Ningyu Zhang","publication_date":"2026-05-28","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Ongoing work","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.30434","date_added":""},{"row_id":"ale-0487","title":"MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks","url":"https://arxiv.org/abs/2602.16313","canonical_url":"https://arxiv.org/abs/2602.16313","annotation":"Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","key_contribution":"Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","novelty":"The work turns loop quality into a measurable task or score. Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","impact":"Use MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Zexue He; Yu Wang; Churan Zhi; Yuanzhe Hu; Tzu-Ping Chen; Lang Yin; Ze Chen; Tong Arthur Wu; Siru Ouyang; Zihan Wang; Jiaxin Pei; Julian McAuley; Yejin Choi; Alex Pentland","publication_date":"2026-02-18","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2602.16313","date_added":""},{"row_id":"ale-0488","title":"Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations","url":"https://arxiv.org/abs/2606.00832","canonical_url":"https://arxiv.org/abs/2606.00832","annotation":"Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","key_contribution":"Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","impact":"Use Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;context;verification;state;exit","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Adril Putra Merin; David Anugraha; Ayu Purwarianti; Genta Indra Winata","publication_date":"2026-05-30","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Preprint","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.00832","date_added":""},{"row_id":"ale-0489","title":"π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows","url":"https://arxiv.org/abs/2605.14678","canonical_url":"https://arxiv.org/abs/2605.14678","annotation":"Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","key_contribution":"Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","novelty":"The work turns loop quality into a measurable task or score. Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","impact":"Use π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Haoran Zhang; Luxin Xu; Zhilin Wang; Runquan Gui; Shunkai Zhang; Haodi Lei; Zihao He; Bingsu He; Chicheng Qin; Tong Zhu; Xiaoye Qu; Yang Yang; Yu Cheng; Yafu Li","publication_date":"2026-05-14","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"44 pages","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.14678","date_added":""},{"row_id":"ale-0490","title":"Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation","url":"https://arxiv.org/abs/2603.23638","canonical_url":"https://arxiv.org/abs/2603.23638","annotation":"A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","key_contribution":"A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","novelty":"The work targets tasks that exceed a single context window or prompt session. A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","impact":"Use Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"state","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Yi Han; Yan Wang; Lingfei Qian; Haohang Li; Yupeng Cao; Yueru He; Xueqing Peng; Nanhan Shen; Yitao Xu; Yankai Chen; Dongji Feng; Jimin Huang; Xue Liu; Jian-Yun Nie; Sophia Ananiadou","publication_date":"2026-03-24","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.23638","date_added":""},{"row_id":"ale-0491","title":"EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer","url":"https://arxiv.org/abs/2607.05202","canonical_url":"https://arxiv.org/abs/2607.05202","annotation":"Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","key_contribution":"Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","impact":"Use EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Xingze Gao; Chuanrui Hu; Hongda Chen; Pengfei Yao; Zhao Wang; Yi Bai; Zhengwei Wu; Yunyun Han; Xiaofeng Cong; Jie Gui; Yafeng Deng; Teng Li","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"15 pages, 2 figures, 8 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05202","date_added":""},{"row_id":"ale-0492","title":"AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents","url":"https://arxiv.org/abs/2607.02255","canonical_url":"https://arxiv.org/abs/2607.02255","annotation":"Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","key_contribution":"Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","novelty":"Persistent memory is treated as an external runtime artifact. Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","impact":"Use AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;state","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Xiangchen Cheng; Yunwei Jiang; Jianwen Sun; Zizhen Li; Chuanhao Li; Xiangcheng Cao; Yihao Liu; Fanrui Zhang; Li Jin; Kaipeng Zhang","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02255","date_added":""},{"row_id":"ale-0493","title":"Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops","url":"https://arxiv.org/abs/2607.05197","canonical_url":"https://arxiv.org/abs/2607.05197","annotation":"Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","key_contribution":"Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","impact":"Use Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.05197; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"delegation;verification;budget","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Tobias Kiecker; Eik Reichmann; Hosung Kang; Gabin An; Lars Grunske","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"4 Pages (+1 for references), NIER Paper","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05197","date_added":""},{"row_id":"ale-0494","title":"DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks","url":"https://arxiv.org/abs/2607.07946","canonical_url":"https://arxiv.org/abs/2607.07946","annotation":"113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","key_contribution":"113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","novelty":"The work targets tasks that exceed a single context window or prompt session. 113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","impact":"Use DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Wenqi Huang; Charley Lee; Leonard Tng; Serena Ge","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"32 pages, 10 figures. Code and data: https://github.com/datacurve-ai/deep-swe ; https://deepswe.datacurve.ai/","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07946","date_added":""},{"row_id":"ale-0495","title":"PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization","url":"https://arxiv.org/abs/2607.07744","canonical_url":"https://arxiv.org/abs/2607.07744","annotation":"Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","key_contribution":"Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","novelty":"Verification is promoted from a final check to a loop-control signal. Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","impact":"Use PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Yingyun Cui; Yi Xie; Piaohong Wang; Jiawei Ma; Bo Liu; Liangliang Cao","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07744","date_added":""},{"row_id":"ale-0496","title":"Benchmarking coding agents on Databricks' multi-million line codebase","url":"https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase","canonical_url":"https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase","annotation":"Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","key_contribution":"Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","novelty":"Verification is promoted from a final check to a loop-control signal. Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","impact":"Use Benchmarking coding agents on Databricks' multi-million line codebase to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;verification;budget","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"","publication_date":"2026","publication_year":"2026","publication_venue":"","publisher":"Databricks","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0497","title":"UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks","url":"https://arxiv.org/abs/2607.08768","canonical_url":"https://arxiv.org/abs/2607.08768","annotation":"Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","key_contribution":"Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","impact":"Use UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;context;verification;state;escalation","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Zhekai Chen; Chengqi Duan; Kaiyue Sun; Bohao Li; Yuqing Wang; Manyuan Zhang; Xihui Liu","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Project Page: https://uniclawbench.github.io | GitHub Repo: https://github.com/HKU-MMLab/UniClawBench","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08768","date_added":""},{"row_id":"ale-0498","title":"SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills","url":"https://arxiv.org/abs/2607.09016","canonical_url":"https://arxiv.org/abs/2607.09016","annotation":"Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","key_contribution":"Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","novelty":"The work turns loop quality into a measurable task or score. Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","impact":"Use SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Xuan Chen; Chengpeng Wang; Lu Yan; Xiangyu Zhang","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09016","date_added":""},{"row_id":"ale-0499","title":"SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution","url":"https://arxiv.org/abs/2603.13428","canonical_url":"https://arxiv.org/abs/2603.13428","annotation":"Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","key_contribution":"Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","impact":"Use SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Gangda Deng; Zhaoling Chen; Zhongming Yu; Haoyang Fan; Yuhong Liu; Yuxin Yang; Dhruv Parikh; Rajgopal Kannan; Le Cong; Mengdi Wang; Qian Zhang; Viktor Prasanna; Xiangru Tang; Xingyao Wang","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 43rd International Conference on Machine Learning (ICML)","publisher":"PMLR","doi":"","publication_note":"Accepted at Proceedings of the 43rd International Conference on Machine Learning (ICML); the linked arXiv record is the available paper version.","primary_category":"cs.SE","metadata_source":"Current arXiv acceptance note and official project record","github_repo":"","github_stars":"","arxiv_id":"2603.13428","date_added":""},{"row_id":"ale-0500","title":"AgentAbstain: Do LLM Agents Know When Not to Act?","url":"https://arxiv.org/abs/2607.10059","canonical_url":"https://arxiv.org/abs/2607.10059","annotation":"Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","key_contribution":"Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","novelty":"The work turns loop quality into a measurable task or score. Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","impact":"Use AgentAbstain: Do LLM Agents Know When Not to Act? to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Xun Liu; Yi Evie Zhang; Vira Kasprova; Parisa Rabbani; Pardis Sadat Zahraei; Tianyu Zhang; Ali Ebrahimpour-Boroojeny; Varun Chandrasekaran","publication_date":"2026-07-11","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"56 pages, 13 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.10059","date_added":"2026-07-15"},{"row_id":"ale-0501","title":"Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy","url":"https://arxiv.org/abs/2607.10526","canonical_url":"https://arxiv.org/abs/2607.10526","annotation":"Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","key_contribution":"Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","novelty":"The work turns loop quality into a measurable task or score. Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","impact":"Use Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;verification;state","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Xutao Mao; Liangjie Zhao; Leyao Wang; Rui Qian; Qiang Huang; Wentao Wang; Bo Han; Xiang Zheng; Cong Wang","publication_date":"2026-07-12","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.10526","date_added":"2026-07-15"},{"row_id":"ale-0502","title":"Set-shifting Behavioral Test for Harnessed Agents","url":"https://arxiv.org/abs/2607.13396","canonical_url":"https://arxiv.org/abs/2607.13396","annotation":"Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","key_contribution":"Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","impact":"Use Set-shifting Behavioral Test for Harnessed Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Ziwei Ye","publication_date":"2026-07-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.13396","date_added":"2026-07-17"},{"row_id":"ale-0503","title":"MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers","url":"https://arxiv.org/abs/2607.14642","canonical_url":"https://arxiv.org/abs/2607.14642","annotation":"Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","key_contribution":"Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","impact":"Use MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Huanxi Liu; Kun Hu; Jiaqi Liao; Qiang Wang; Pengfei Qian; YuanZhao Zhai; Dawei Feng; Bo Ding; Huaimin Wang","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.14642","date_added":"2026-07-17"},{"row_id":"ale-0504","title":"MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization","url":"https://arxiv.org/abs/2607.15205","canonical_url":"https://arxiv.org/abs/2607.15205","annotation":"Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","key_contribution":"Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","novelty":"The work turns loop quality into a measurable task or score. Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","impact":"Use MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Shaoxiong Zhan; Shi Hu; Boyu Feng; Hai Lin; Andrew Gong; Zhengda Zhou; Jiaying Zhou; Yunyun Hou; Hao Su; Hai-Tao Zheng","publication_date":"2026-07-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.15205","date_added":"2026-07-17"},{"row_id":"ale-0505","title":"MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interaction and MCP-Augmented Tasks","url":"https://aclanthology.org/2026.acl-long.278/","canonical_url":"https://aclanthology.org/2026.acl-long.278/","annotation":"Evaluates long-horizon mobile work that requires clarification and MCP tool use in reproducible self-hosted environments with deterministic backend, storage, and callback verification.","key_contribution":"Evaluates long-horizon mobile work that requires clarification and MCP tool use in reproducible self-hosted environments with deterministic backend, storage, and callback verification.","novelty":"Verification is promoted from a final check to a loop-control signal. Evaluates long-horizon mobile work that requires clarification and MCP tool use in reproducible self-hosted environments with deterministic backend, storage, and callback verification.","impact":"Use MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interaction and MCP-Augmented Tasks to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Quyu Kong; Xu Zhang; Zhenyu Yang; Nolan Gao; Chen Liu; Panrong Tong; Chenglin Cai; Hanzhang Zhou; Jianan Zhang; Liangyu Chen; Zhidan Liu; Steven Hoi; Yue Wang","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","publisher":"ACL Anthology","doi":"10.18653/v1/2026.acl-long.278","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0506","title":"AGENCYBENCH: Benchmarking the Frontiers of Autonomous Agents","url":"https://aclanthology.org/2026.acl-long.337/","canonical_url":"https://aclanthology.org/2026.acl-long.337/","annotation":"Provides 138 high-fidelity tasks across six capabilities whose average rollout exceeds one million tokens and 90 tool calls, exposing context-retention and long-horizon execution limits.","key_contribution":"Provides 138 high-fidelity tasks across six capabilities whose average rollout exceeds one million tokens and 90 tool calls, exposing context-retention and long-horizon execution limits.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Provides 138 high-fidelity tasks across six capabilities whose average rollout exceeds one million tokens and 90 tool calls, exposing context-retention and long-horizon execution limits.","impact":"Use AGENCYBENCH: Benchmarking the Frontiers of Autonomous Agents to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;context;budget","audience":"researcher;evaluator","loop_layer":"evaluation","scope_fit":"enabling","evidence_class":"benchmark","evidence_tier":"A","signal_strength":"high","source_status":"ok","authors":"Keyu Li; Junhao Shi; Yang Xiao; Mohan Jiang; Jie Sun; Yunze Wu; Dayuan Fu; Shijie Xia; Xiaojie Cai; Tianze Xu; Weiye Si; Wenjie Li; Dequan Wang; Pengfei Liu","publication_date":"2026","publication_year":"2026","publication_venue":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","publisher":"ACL Anthology","doi":"10.18653/v1/2026.acl-long.337","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":"2026-07-18"},{"row_id":"ale-0507","title":"Agentic Engineering: The Agent Loop","url":"https://junpingyi.com/books/agentic-engineering/agent-loop/","canonical_url":"https://junpingyi.com/books/agentic-engineering/agent-loop/","annotation":"Minimal mental model for the loop underlying agent operation.","key_contribution":"Minimal mental model for the loop underlying agent operation.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Minimal mental model for the loop underlying agent operation.","impact":"Use Agentic Engineering: The Agent Loop to bound risk before recurring or unattended execution.","signal":"Contextual source from junpingyi.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake;budget;escalation;exit","audience":"operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"junpingyi.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0508","title":"The agent loop: ReAct, plan-and-execute, reflection","url":"https://www.kunwar.page/chapter/067-the-agent-loop-react-plan-and-execute-reflection","canonical_url":"https://www.kunwar.page/chapter/067-the-agent-loop-react-plan-and-execute-reflection","annotation":"Practical walkthrough of the base loop and common variants.","key_contribution":"Practical walkthrough of the base loop and common variants.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Practical walkthrough of the base loop and common variants.","impact":"Use The agent loop: ReAct, plan-and-execute, reflection to bound risk before recurring or unattended execution.","signal":"Contextual source from www.kunwar.page; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake;budget;escalation;exit","audience":"operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"kunwar.page","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0509","title":"How to Build an Agent","url":"https://ampcode.com/how-to-build-an-agent","canonical_url":"https://ampcode.com/notes/how-to-build-an-agent","annotation":"Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","key_contribution":"Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","impact":"Use How to Build an Agent to bound risk before recurring or unattended execution.","signal":"Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"budget","audience":"operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"ampcode.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0510","title":"Agentic Coding Recommendations","url":"https://lucumr.pocoo.org/2025/6/12/agentic-coding/","canonical_url":"https://lucumr.pocoo.org/2025/6/12/agentic-coding/","annotation":"Armin Ronacher's field notes on which practices hold up when agents do most of the work.","key_contribution":"Armin Ronacher's field notes on which practices hold up when agents do most of the work.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Armin Ronacher's field notes on which practices hold up when agents do most of the work.","impact":"Use Agentic Coding Recommendations to bound risk before recurring or unattended execution.","signal":"Contextual source from lucumr.pocoo.org; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake;budget;escalation;exit","audience":"operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2025-06-12","publication_year":"2025","publication_venue":"","publisher":"Armin Ronacher's Thoughts and Writings","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0511","title":"Coding Agents 101: The Art of Actually Getting Things Done","url":"https://devin.ai/agents101","canonical_url":"https://devin.ai/agents101","annotation":"Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.","key_contribution":"Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.","impact":"Use Coding Agents 101: The Art of Actually Getting Things Done to bound risk before recurring or unattended execution.","signal":"Contextual source from devin.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"delegation;exit","audience":"operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"devin.ai","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0512","title":"How Anthropic teams use Claude Code","url":"https://claude.com/blog/how-anthropic-teams-use-claude-code","canonical_url":"https://claude.com/blog/how-anthropic-teams-use-claude-code","annotation":"Cross-team field report of real recurring agent workflows in engineering, security, and data science.","key_contribution":"Cross-team field report of real recurring agent workflows in engineering, security, and data science.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Cross-team field report of real recurring agent workflows in engineering, security, and data science.","impact":"Use How Anthropic teams use Claude Code to bound risk before recurring or unattended execution.","signal":"Contextual source from claude.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake;budget;escalation;exit","audience":"operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Claude","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0513","title":"How Boris Uses Claude Code","url":"https://howborisusesclaudecode.com/","canonical_url":"https://howborisusesclaudecode.com/","annotation":"Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","key_contribution":"Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","impact":"Use How Boris Uses Claude Code to bound risk before recurring or unattended execution.","signal":"Contextual source from howborisusesclaudecode.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"objective;trigger;workspace;exit","audience":"operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"@CarolinaCherry","publication_date":"","publication_year":"","publication_venue":"","publisher":"How Boris Uses Claude Code","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0514","title":"Agent of the Day: Copilot Agent PR Analysis","url":"https://github.github.com/gh-aw/blog/2026-05-26-agent-of-the-day/","canonical_url":"https://github.github.com/gh-aw/blog/2026-05-26-agent-of-the-day/","annotation":"Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","key_contribution":"Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","novelty":"Primary-source operational guidance rather than commentary. Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","impact":"Use Agent of the Day: Copilot Agent PR Analysis to bound risk before recurring or unattended execution.","signal":"Contextual source from github.github.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake","audience":"operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"GitHub Agentic Workflows","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0515","title":"Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows","url":"https://arxiv.org/abs/2607.07052","canonical_url":"https://arxiv.org/abs/2607.07052","annotation":"Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","key_contribution":"Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","impact":"Use Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07052; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"budget","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Arun Malik","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Conference-style paper; 10 pages (estimated from manuscript formatting if applicable); focuses on agentic AI, AIOps, workflow automation, deterministic execution, and LLM cost optimization","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07052","date_added":""},{"row_id":"ale-0516","title":"Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems","url":"https://arxiv.org/abs/2607.08010","canonical_url":"https://arxiv.org/abs/2607.08010","annotation":"Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.","key_contribution":"Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.","impact":"Use Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.08010; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"intake;workspace","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Kalle Kujanpää; Ning Liu; Shahnawaz Alam; Yeshwanth Reddy Sura; Tianyu Yang; Kristina Klinkner; Shervin Malmasi","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Preprint","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08010","date_added":""},{"row_id":"ale-0517","title":"AI Loop Engineering: Build Autonomous Agents with Claude Code /goal and Routines","url":"https://www.sabrina.dev/p/loop-engineering-claude-code-goal-routines","canonical_url":"https://www.sabrina.dev/p/loop-engineering-claude-code-goal-routines","annotation":"Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.","key_contribution":"Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.","novelty":"Verification is promoted from a final check to a loop-control signal. Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.","impact":"Use AI Loop Engineering: Build Autonomous Agents with Claude Code /goal and Routines to bound risk before recurring or unattended execution.","signal":"Contextual source from www.sabrina.dev; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"objective;verification;state","audience":"operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Sabrina Ramonov 🍄","publication_date":"","publication_year":"","publication_venue":"","publisher":"sabrina.dev","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0518","title":"Agent Delivery Engineering Predictive Reliability Framework","url":"https://arxiv.org/abs/2607.07689","canonical_url":"https://arxiv.org/abs/2607.07689","annotation":"Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.","key_contribution":"Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.","impact":"Use Agent Delivery Engineering Predictive Reliability Framework to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07689; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"delegation","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Dexing Liu","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"117pages,83figures","primary_category":"cs.MA","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07689","date_added":""},{"row_id":"ale-0519","title":"rocketplaneIO","url":"https://github.com/olemeyer/rocketplaneIO","canonical_url":"https://github.com/olemeyer/rocketplaneIO","annotation":"Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.","key_contribution":"Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.","impact":"Use rocketplaneIO to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (141 stars; 3 forks; Apache-2.0 license; updated 2026-07-15); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"trigger;intake;budget;escalation;exit","audience":"builder;operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"source-implementation","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"olemeyer/rocketplaneIO","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"olemeyer/rocketplaneIO","github_stars":"141","arxiv_id":"","date_added":""},{"row_id":"ale-0520","title":"Migrating a Production AI Agent to GPT-5.6","url":"https://ploy.ai/blog/migrating-a-production-ai-agent-to-gpt-5-6","canonical_url":"https://ploy.ai/blog/migrating-a-production-ai-agent-to-gpt-5-6","annotation":"Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.","key_contribution":"Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.","impact":"Use Migrating a Production AI Agent to GPT-5.6 to bound risk before recurring or unattended execution.","signal":"Contextual source from ploy.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"workspace;verification;state;exit","audience":"operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Ploy","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0521","title":"Coding-agents can replicate scientific machine learning papers","url":"https://arxiv.org/abs/2607.02134","canonical_url":"https://arxiv.org/abs/2607.02134","annotation":"Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.","key_contribution":"Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.","novelty":"Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.","impact":"Use Coding-agents can replicate scientific machine learning papers to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.02134; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Operations Playbooks","section_slug":"operations-playbooks","lifecycle_stages":"workspace;exit","audience":"researcher;evaluator;operator;security","loop_layer":"operations","scope_fit":"direct","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Atharva Hans; Ilias Bilionis","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02134","date_added":"2026-07-17"},{"row_id":"ale-0522","title":"Resource entry template","url":"templates/resource-entry.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/templates/resource-entry.md","annotation":"Format for adding a single resource with evidence quality and category fit.","key_contribution":"Format for adding a single resource with evidence quality and category fit.","novelty":"The resource is directly reusable as a starting artifact. Format for adding a single resource with evidence quality and category fit.","impact":"Use Resource entry template to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Template","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Templates And Patterns","section_slug":"templates-and-patterns","lifecycle_stages":"whole-loop","audience":"builder;operator","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0523","title":"Loop pattern template","url":"templates/loop-pattern.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/templates/loop-pattern.md","annotation":"Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.","key_contribution":"Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.","novelty":"The resource is directly reusable as a starting artifact. Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.","impact":"Use Loop pattern template to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Template","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Templates And Patterns","section_slug":"templates-and-patterns","lifecycle_stages":"whole-loop","audience":"builder;operator","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0524","title":"Loop contract schema","url":"schemas/loop-contract.schema.json","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/schemas/loop-contract.schema.json","annotation":"Machine-readable schema for portable loop specs.","key_contribution":"Machine-readable schema for portable loop specs.","novelty":"The contribution is machine-readable and validation-friendly. 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Dependency-free demo that validates and renders a loop contract JSON file.","impact":"Use Loop contract preview script to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Template","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Templates And Patterns","section_slug":"templates-and-patterns","lifecycle_stages":"whole-loop","audience":"builder;operator","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0526","title":"Translation guide","url":"TRANSLATIONS.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/TRANSLATIONS.md","annotation":"How to add or maintain a language translation without drifting from the canonical English list.","key_contribution":"How to add or maintain a language translation without drifting from the canonical English list.","novelty":"The resource is directly reusable as a starting artifact. How to add or maintain a language translation without drifting from the canonical English list.","impact":"Use Translation guide to reuse a concrete artifact or connect it to the wider ecosystem.","signal":"Local artifact maintained with automated validation checks.","resource_type":"Template","collection":"Apply","user_goal":"Reuse, adapt, and contribute concrete loop artifacts.","section":"Templates And Patterns","section_slug":"templates-and-patterns","lifecycle_stages":"whole-loop","audience":"builder;operator","loop_layer":"workflow","scope_fit":"direct","evidence_class":"repository-native","evidence_tier":"A","signal_strength":"medium","source_status":"local_ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"GitHub","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"repository","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0527","title":"Pattern library index","url":"patterns/README.md","canonical_url":"https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/README.md","annotation":"Practical loop patterns with triggers, state, verification gates, budgets, and escalation paths.","key_contribution":"Practical loop patterns with triggers, state, verification gates, budgets, and escalation paths.","novelty":"Verification is promoted from a final check to a loop-control signal. 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Compares self-correction patterns and their cost/failure tradeoffs.","impact":"Use Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared to bound risk before recurring or unattended execution.","signal":"Contextual source from callsphere.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification;budget","audience":"operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"risk-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"CallSphere","publication_date":"2026-04-24","publication_year":"2026","publication_venue":"","publisher":"CallSphere","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0540","title":"How to Build an AI Agent Harness: A 2026 Complete Guide","url":"https://atlan.com/know/how-to-build-ai-agent-harness/","canonical_url":"https://atlan.com/know/how-to-build-ai-agent-harness/","annotation":"Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","key_contribution":"Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","impact":"Use How to Build an AI Agent Harness: A 2026 Complete Guide to bound risk before recurring or unattended execution.","signal":"Contextual source from atlan.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"workspace;context;verification","audience":"operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"risk-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"atlan.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0541","title":"Harness Engineering vs Prompt Engineering vs Context Engineering Explained","url":"https://medium.com/@visrow/harness-engineering-vs-prompt-engineering-vs-context-engineering-explained-0423b692c87d","canonical_url":"https://medium.com/@visrow/harness-engineering-vs-prompt-engineering-vs-context-engineering-explained-0423b692c87d","annotation":"Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.","key_contribution":"Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.","impact":"Use Harness Engineering vs Prompt Engineering vs Context Engineering Explained to bound risk before recurring or unattended execution.","signal":"Contextual source from medium.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"context","audience":"operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"risk-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Vishal Mysore","publication_date":"2026-05-19","publication_year":"2026","publication_venue":"","publisher":"Medium","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0542","title":"Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering","url":"https://arxiv.org/abs/2606.17799","canonical_url":"https://arxiv.org/abs/2606.17799","annotation":"Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","key_contribution":"Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","novelty":"The work turns loop quality into a measurable task or score. Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","impact":"Use Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2606.17799; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification","audience":"researcher;evaluator;operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Maria I. Gorinova; Macey Baker; Amy Heineike; Maksim Shaposhnikov; Rob Willoughby; Dru Knox","publication_date":"2026-06-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.17799","date_added":""},{"row_id":"ale-0543","title":"Understanding the Challenges in Iterative Generative Optimization with LLMs","url":"https://arxiv.org/abs/2603.23994","canonical_url":"https://arxiv.org/abs/2603.23994","annotation":"Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","key_contribution":"Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","impact":"Use Understanding the Challenges in Iterative Generative Optimization with LLMs to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2603.23994; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget;escalation;exit","audience":"researcher;evaluator;operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Allen Nie; Xavier Daull; Zhiyi Kuang; Abhinav Akkiraju; Anish Chaudhuri; Max Piasevoli; Ryan Rong; YuCheng Yuan; Prerit Choudhary; Shannon Xiao; Rasool Fakoor; Adith Swaminathan; Ching-An Cheng","publication_date":"2026-03-25","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"39 pages, 17 figures","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.23994","date_added":""},{"row_id":"ale-0544","title":"The Illusion of Multi-Agent Advantage","url":"https://arxiv.org/abs/2606.13003","canonical_url":"https://arxiv.org/abs/2606.13003","annotation":"Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","key_contribution":"Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","impact":"Use The Illusion of Multi-Agent Advantage to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2606.13003; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator;operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Prathyusha Jwalapuram; Hehai Lin; Chuyuan Li; Fangkai Jiao; Sudong Wang; Yifei Ming; Zixuan Ke; Chengwei Qin; Giuseppe Carenini; Shafiq Joty","publication_date":"2026-06-11","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.13003","date_added":""},{"row_id":"ale-0545","title":"The Coming Loop","url":"https://lucumr.pocoo.org/2026/6/23/the-coming-loop/","canonical_url":"https://lucumr.pocoo.org/2026/6/23/the-coming-loop/","annotation":"Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","key_contribution":"Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","impact":"Use The Coming Loop to bound risk before recurring or unattended execution.","signal":"Contextual source from lucumr.pocoo.org; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"escalation;exit","audience":"operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"risk-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-06-23","publication_year":"2026","publication_venue":"","publisher":"Armin Ronacher's Thoughts and Writings","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0546","title":"Loop Engineering, the Latest AI Buzzword, Still Needs Humans in the Loop","url":"https://www.theregister.com/ai-and-ml/2026/06/24/loop-engineering-latest-ai-buzzword-still-needs-humans-in-the-loop/5261735","canonical_url":"https://www.theregister.com/ai-and-ml/2026/06/24/loop-engineering-latest-ai-buzzword-still-needs-humans-in-the-loop/5261735","annotation":"The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","key_contribution":"The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","impact":"Use Loop Engineering, the Latest AI Buzzword, Still Needs Humans in the Loop to bound risk before recurring or unattended execution.","signal":"Contextual source from www.theregister.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget","audience":"operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"risk-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"","publisher":"theregister","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0547","title":"When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents","url":"https://arxiv.org/abs/2607.01641","canonical_url":"https://arxiv.org/abs/2607.01641","annotation":"Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","key_contribution":"Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","impact":"Use When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.01641; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"workspace;delegation;exit","audience":"researcher;evaluator;operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xinyi Hou; Shenao Wang; Yanjie Zhao; Haoyu Wang","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.01641","date_added":""},{"row_id":"ale-0548","title":"The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents","url":"https://arxiv.org/abs/2607.07436","canonical_url":"https://arxiv.org/abs/2607.07436","annotation":"Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.","key_contribution":"Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.","impact":"Use The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07436; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget;escalation;exit","audience":"researcher;evaluator;operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Xing Zhang; Yanwei Cui; Guanghui Wang; Ziyuan Li; Wei Qiu; Bing Zhu; Peiyang He","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07436","date_added":""},{"row_id":"ale-0549","title":"Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows","url":"https://arxiv.org/abs/2607.07504","canonical_url":"https://arxiv.org/abs/2607.07504","annotation":"Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","key_contribution":"Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","impact":"Use Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07504; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget;escalation;exit","audience":"researcher;evaluator;operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-paper","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Wei-Jung Huang","publication_date":"2026","publication_year":"2026","publication_venue":"KDD Workshop on AI Data Scientist","publisher":"ACM SIGKDD","doi":"","publication_note":"Accepted at KDD Workshop on AI Data Scientist; the linked arXiv record is the available paper version.","primary_category":"cs.AI","metadata_source":"Current arXiv acceptance note and official workshop page","github_repo":"","github_stars":"","arxiv_id":"2607.07504","date_added":""},{"row_id":"ale-0550","title":"The Verification Horizon: No Silver Bullet for Coding Agent Rewards","url":"https://arxiv.org/abs/2606.26300","canonical_url":"https://arxiv.org/abs/2606.26300","annotation":"Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","key_contribution":"Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","novelty":"Verification is promoted from a final check to a loop-control signal. Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","impact":"Use The Verification Horizon: No Silver Bullet for Coding Agent Rewards to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2606.26300; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator;operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Binghai Wang; Chenlong Zhang; Dayiheng Liu; Jiajun Zhang; Jiawei Chen; Mingze Li; Mouxiang Chen; Rongyao Fang; Siyuan Zhang; Xuwu Wang; Yuheng Jing; Zeyao Ma; Zeyu Cui","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Authors are listed alphabetically by their first names","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.26300","date_added":""},{"row_id":"ale-0551","title":"Write Code Like a Human Will Maintain It","url":"https://unstack.io/write-code-like-a-human-will-maintain-it","canonical_url":"https://unstack.io/write-code-like-a-human-will-maintain-it","annotation":"Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","key_contribution":"Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","impact":"Use Write Code Like a Human Will Maintain It to bound risk before recurring or unattended execution.","signal":"Contextual source from unstack.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"escalation","audience":"operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"risk-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"","publisher":"Unstack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0552","title":"Claude Code Sends 33k Tokens Before Reading the Prompt","url":"https://systima.ai/blog/claude-code-vs-opencode-token-overhead","canonical_url":"https://systima.ai/blog/claude-code-vs-opencode-token-overhead","annotation":"July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","key_contribution":"July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","impact":"Use Claude Code Sends 33k Tokens Before Reading the Prompt to bound risk before recurring or unattended execution.","signal":"Contextual source from systima.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"delegation;budget","audience":"operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"practitioner-analysis","evidence_tier":"B","signal_strength":"contextual","source_status":"ok","authors":"Systima","publication_date":"2026-07-12","publication_year":"2026","publication_venue":"","publisher":"Systima","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0553","title":"Rethinking the Evaluation of Harness Evolution for Agents","url":"https://arxiv.org/abs/2607.12227","canonical_url":"https://arxiv.org/abs/2607.12227","annotation":"Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","key_contribution":"Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","impact":"Use Rethinking the Evaluation of Harness Evolution for Agents to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.12227; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification;budget","audience":"researcher;evaluator;operator;security","loop_layer":"cross-layer","scope_fit":"enabling","evidence_class":"research-preprint","evidence_tier":"A","signal_strength":"medium","source_status":"ok","authors":"Yike Wang; Huaisheng Zhu; Zhengyu Hu; Yige Yuan; Zhengyu Chen; Shakti Senthil; Hannaneh Hajishirzi; Yulia Tsvetkov; Pradeep Dasigi; Teng Xiao","publication_date":"2026-07-14","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.12227","date_added":"2026-07-17"},{"row_id":"ale-0554","title":"Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes","url":"https://arxiv.org/abs/2607.13071","canonical_url":"https://arxiv.org/abs/2607.13071","annotation":"Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","key_contribution":"Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","novelty":"Verification is promoted from a final check to a loop-control signal. 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