Add paper link and task category to dataset card

README.md

@@ -1,21 +1,25 @@
 ---
-license: cc0-1.0
 language:
-  - en
+- en
+license: cc0-1.0
 pretty_name: Awesome Loop Engineering
 tags:
-  - loop-engineering
-  - ai-agents
-  - coding-agents
-  - agentic-workflows
-  - awesome-list
+- loop-engineering
+- ai-agents
+- coding-agents
+- agentic-workflows
+- awesome-list
 configs:
-  - config_name: resources
-    data_files:
-      - split: train
-        path: data/resources.jsonl
+- config_name: resources
+  data_files:
+  - split: train
+    path: data/resources.jsonl
+task_categories:
+- other
 ---
 
+This dataset is associated with the paper [EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer](https://huggingface.co/papers/2607.05202).
+
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
 </p>
@@ -54,7 +58,7 @@ configs:
   <a href="https://huggingface.co/datasets/cy0307/awesome-loop-engineering">Hugging Face dataset</a>
 </p>
 
-Awesome Loop Engineering is a curated, implementation-oriented field guide to **Loop Engineering**: the layer above prompt, context, and harness engineering for designing recurring AI-agent systems.
+Awesome Loop Engineering is a curated, implementation-oriented field guide to **Loop Engineering**: the layer above prompt, context, and harness engineering for designing recurring AI-agent loops.
 
 Prompt engineering improves what you ask the model. Context engineering improves what the model can see. Harness engineering improves the tools, permissions, sandboxes, and checks around one agent run. **Loop Engineering sits above all three**: it is the emerging AI and coding-agent practice of moving from manually prompting agents turn by turn to designing loops that do the prompting, supervision, verification, state updates, and re-triggering for you.
 
@@ -267,8 +271,8 @@ Direct resources about the new AI/coding-agent meaning of Loop Engineering.
 
 A useful loop has a contract. If one of these is missing, the loop usually becomes either a manual prompt habit or an unsafe background automation. Prompt, context, and harness choices are ingredients; the loop contract is the operating layer that connects them over time.
 
-<p align="center">
-  <img src="assets/loop-contract-cards.svg" alt="Loop Contract cards: objective, trigger, intake, workspace, context, delegation, verification, state, budget, escalation, and exit" width="100%">
+<p align="center\">
+  <img src=\"assets/loop-contract-cards.svg\" alt=\"Loop Contract cards: objective, trigger, intake, workspace, context, delegation, verification, state, budget, escalation, and exit\" width=\"100%\">
 </p>
 
 | Part              | Design question                        | Common artifact                                              |
@@ -580,7 +584,7 @@ This section focuses on durable loop state and cross-run context. For context-wi
 - 📄 **Paper** [Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers](https://arxiv.org/abs/2603.07670) - Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.
 - 📄 **Paper** [Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering](https://arxiv.org/abs/2604.08224) - 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.
 - 📄 **Paper** [Meta Context Engineering via Agentic Skill Evolution](https://arxiv.org/abs/2601.21557) - 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).
-- 📄 **Paper** [Are We Ready for an Agent-Native Memory System?](https://arxiv.org/abs/2606.24775) - 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.
+- 📄 **Paper** [Are We Ready for an Agent-Native Memory System?](https://arxiv.org/abs/2606.24775) - 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.
 - 📄 **Paper** [Self-Evolving World Models for LLM Agent Planning](https://arxiv.org/abs/2606.30639) - 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.
 - 📄 **Paper** [Rethinking Continual Experience Internalization for Self-Evolving LLM Agents](https://arxiv.org/abs/2606.04703) - 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.
 - 🧰 **Tool** [GenericAgent](https://github.com/lsdefine/GenericAgent) - 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.
@@ -810,4 +814,4 @@ If this repository is useful in your work, please cite it with:
 
 **Reusable blurb** (for blog posts, talks, internal docs, or community posts):
 
-> Loop Engineering is the practice of designing recurring AI-agent and coding-agent systems that discover work, delegate to agents, verify results, persist state, and retry or escalate on a cadence or until a goal is reached. *Awesome Loop Engineering* is a curated, implementation-focused resource collection for this practice: [github.com/ChaoYue0307/awesome-loop-engineering](https://github.com/ChaoYue0307/awesome-loop-engineering)
+> Loop Engineering is the practice of designing recurring AI-agent and coding-agent systems that discover work, delegate to agents, verify results, persist state, and retry or escalate on a cadence or until a goal is reached. *Awesome Loop Engineering* is a curated, implementation-focused resource collection for this practice: [github.com/ChaoYue0307/awesome-loop-engineering](https://github.com/ChaoYue0307/awesome-loop-engineering)