Datasets:

SaylorTwift
/

llm-benchmark-usage

@@ -128,3 +128,11 @@
 {"name": "PRBench-Legal", "categories": ["domain_specific"], "modality": ["text"], "language": ["en"], "description": "The legal split of PRBench, a large-scale expert-authored benchmark for high-stakes professional reasoning: tasks are contributed by JD-holding professionals modeling their own real workflows (not academic exercises) and graded against expert-curated rubrics (10-30 weighted criteria per task) rather than free-form judging; PRBench totals 1,100 expert-authored tasks and 19,356 rubric criteria across its Legal and Finance splits.", "paper_url": "https://arxiv.org/abs/2511.11562", "implementation_url": "https://github.com/scaleapi/PRBench"}
 {"name": "PRBench-Finance", "categories": ["domain_specific"], "modality": ["text"], "language": ["en"], "description": "The finance split of PRBench, a large-scale expert-authored benchmark for high-stakes professional reasoning: tasks are contributed by CFA-holding professionals modeling their own real workflows (not academic exercises) and graded against expert-curated rubrics (10-30 weighted criteria per task) rather than free-form judging; PRBench totals 1,100 expert-authored tasks and 19,356 rubric criteria across its Legal and Finance splits.", "paper_url": "https://arxiv.org/abs/2511.11562", "implementation_url": "https://github.com/scaleapi/PRBench"}
 {"name": "$OneMillion-Bench", "categories": ["long_context"], "modality": ["text"], "language": ["en"], "description": "A long-context benchmark scored via an LLM-judge \"expert score\" rubric (Qwen's card judges it with gemini-3.1-pro-preview); no paper has been published — the only public artifact is an evals-harness CLI repo, so methodology details (task count, context lengths, domains) are not independently verifiable.", "paper_url": "https://github.com/humanlaya/OneMillion-Bench", "implementation_url": "https://github.com/humanlaya/OneMillion-Bench"}

 {"name": "PRBench-Legal", "categories": ["domain_specific"], "modality": ["text"], "language": ["en"], "description": "The legal split of PRBench, a large-scale expert-authored benchmark for high-stakes professional reasoning: tasks are contributed by JD-holding professionals modeling their own real workflows (not academic exercises) and graded against expert-curated rubrics (10-30 weighted criteria per task) rather than free-form judging; PRBench totals 1,100 expert-authored tasks and 19,356 rubric criteria across its Legal and Finance splits.", "paper_url": "https://arxiv.org/abs/2511.11562", "implementation_url": "https://github.com/scaleapi/PRBench"}
 {"name": "PRBench-Finance", "categories": ["domain_specific"], "modality": ["text"], "language": ["en"], "description": "The finance split of PRBench, a large-scale expert-authored benchmark for high-stakes professional reasoning: tasks are contributed by CFA-holding professionals modeling their own real workflows (not academic exercises) and graded against expert-curated rubrics (10-30 weighted criteria per task) rather than free-form judging; PRBench totals 1,100 expert-authored tasks and 19,356 rubric criteria across its Legal and Finance splits.", "paper_url": "https://arxiv.org/abs/2511.11562", "implementation_url": "https://github.com/scaleapi/PRBench"}
 {"name": "$OneMillion-Bench", "categories": ["long_context"], "modality": ["text"], "language": ["en"], "description": "A long-context benchmark scored via an LLM-judge \"expert score\" rubric (Qwen's card judges it with gemini-3.1-pro-preview); no paper has been published — the only public artifact is an evals-harness CLI repo, so methodology details (task count, context lengths, domains) are not independently verifiable.", "paper_url": "https://github.com/humanlaya/OneMillion-Bench", "implementation_url": "https://github.com/humanlaya/OneMillion-Bench"}
+{"name": "FrontierCode 1.1", "categories": ["coding", "agentic"], "modality": ["text"], "language": ["en"], "description": "Cognition's benchmark for code mergeability: whether a coding agent's patch on a real open-source issue is good enough for a maintainer to merge, not just whether it passes tests. 150 tasks across three nested difficulty tiers (Diamond 50 / Main 100 / Extended 150), graded on six axes (behavioral correctness, regression safety, mechanical cleanliness, test correctness, scope discipline, code quality) via an ensemble of unit tests, rubrics, and custom verifiers; no public dataset or paper — only a hosted leaderboard.", "paper_url": "https://cognition.com/blog/frontier-code", "implementation_url": "https://cognition.com/frontiercode"}
+{"name": "Code Arena", "categories": ["coding"], "modality": ["text"], "language": ["en"], "description": "Likely LMArena's \"Code\" category leaderboard: crowd-sourced pairwise comparisons of model-generated code/web apps, ranked via a Bradley-Terry (Elo-style) fit over the vote history, with the Code leaderboard's vote pool and ratings kept independent from LMArena's Text and Vision leaderboards. Not explicitly confirmed as LMArena by the citing source (Google's Gemini 3.7 Flash model card just labels it \"Code Arena\"), so this identification carries some uncertainty.", "paper_url": "https://arxiv.org/abs/2403.04132", "implementation_url": "https://lmarena.ai"}
+{"name": "Terminal-Bench 3.0", "categories": ["agentic", "coding"], "modality": ["text"], "language": ["en"], "description": "The next major version of Terminal-Bench (following 2.0/2.1), raising task difficulty to keep tracking frontier agent capability rather than trailing it — GPU-native workflows, domain-specific professional work, distributed systems, and long-horizon execution, each running in an isolated Docker sandbox. Built as an open community effort: submissions pass an automated review pipeline (static checks, a 35-criteria rubric, Docker/oracle/no-op validation, live agent trials, adversarial cheat trials) before a maintainer accepts them.", "paper_url": "https://www.tbench.ai/benchmarks/terminal-bench-3", "implementation_url": "https://github.com/laude-institute/terminal-bench"}
+{"name": "GDP.pdf", "categories": ["perception", "domain_specific"], "modality": ["text", "image"], "language": ["en"], "description": "Benchmarking Grounded Multimodal Reasoning over Professional PDF Documents: 100 question-document pairs drawn from real professional workflows across ten fields (finance, healthcare, legal, STEM/research, engineering, construction, manufacturing/supply chain, insurance, real estate, HR). Each item was adversarially curated to only include questions that caused a major failure (wrong answer, omitted evidence, fabrication) in at least two frontier models, testing whether a model can read documents like manuals, blueprints, and technical notes and answer without relying on plausible-but-incorrect details.", "paper_url": "https://arxiv.org/abs/2607.11192", "implementation_url": "https://huggingface.co/datasets/surgeai/GDP.pdf"}
+{"name": "CharXiv w/ Tools", "categories": ["perception"], "modality": ["text", "image"], "language": ["en"], "description": "A tool-use evaluation configuration of CharXiv (2,323 real-world charts hand-picked from arXiv papers, paired with human-curated descriptive and reasoning questions) — the original CharXiv release specifies a zero-shot, no-tools setting, so this variant (giving the model tool access during evaluation) is an evaluation-time condition introduced by the citing source (Google's Gemini 3.7 Flash card) rather than an official CharXiv split.", "paper_url": "https://arxiv.org/abs/2406.18521", "implementation_url": "https://github.com/princeton-nlp/CharXiv"}
+{"name": "BioMysteryBench (Human Solvable)", "categories": ["domain_specific"], "modality": ["text"], "language": ["en"], "description": "Anthropic's bioinformatics benchmark: real-world datasets and expert-authored questions with objective, verifiable ground truth (derived from experimental/clinical findings, not unverifiable scientific conclusions), evaluated in a containerized environment with canonical bioinformatics tools, pip/conda install access, and database access (NCBI, Ensembl). The Human-Solvable split (76 questions) covers problems at least one of up to five domain experts answered correctly.", "paper_url": "https://www.anthropic.com/research/Evaluating-Claude-For-Bioinformatics-With-BioMysteryBench", "implementation_url": "https://huggingface.co/datasets/Anthropic/BioMysteryBench-preview"}
+{"name": "BioMysteryBench (Human Difficult)", "categories": ["domain_specific"], "modality": ["text"], "language": ["en"], "description": "Anthropic's bioinformatics benchmark: real-world datasets and expert-authored questions with objective, verifiable ground truth (derived from experimental/clinical findings, not unverifiable scientific conclusions), evaluated in a containerized environment with canonical bioinformatics tools, pip/conda install access, and database access (NCBI, Ensembl). The Human-Difficult split (23 questions) covers problems no expert on a panel of up to five could solve — theoretically solvable but beyond demonstrated human expertise.", "paper_url": "https://www.anthropic.com/research/Evaluating-Claude-For-Bioinformatics-With-BioMysteryBench", "implementation_url": "https://huggingface.co/datasets/Anthropic/BioMysteryBench-preview"}
+{"name": "LAB-Bench 2", "categories": ["domain_specific"], "modality": ["text"], "language": ["en"], "description": "FutureHouse's improved successor to LAB-Bench: nearly 1,900 tasks measuring real-world capabilities of AI systems on useful biology-research tasks (literature search, protocol planning, data analysis, figure/table reasoning, sequence manipulation), calibrated to be meaningfully harder than the original — accuracy on LABBench2 drops 26-46% relative to LAB-Bench across subtasks for the same models.", "paper_url": "https://arxiv.org/abs/2604.09554", "implementation_url": "https://huggingface.co/datasets/futurehouse/labbench2"}