Pyrite benchmarks LLM agents on protein-ligand pose ranking
On 1,500 pose pairs, PyriteFeaturizer reaches 74% accuracy and PyriteReasoner 67%, while expert chemists perform near chance.
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On 1,500 pose pairs, PyriteFeaturizer reaches 74% accuracy and PyriteReasoner 67%, while expert chemists perform near chance.
Harness persistence rarely beats a demonstration cache on memorization, and no harness method gains on unseen tasks, unlike weight updates.
On 200 ICML 2026 papers, six LLMs make nearly all progress in the first 30 of 100 yes-or-no questions.
Of 134 valid game-track models, mean structural F1 is 0.366 and only 28 exactly match the reference.
Busy-agent handling, unseeded policies, and unpublished scoring choices make published CAGE Challenge 4 comparisons hard to reproduce.
DeepSeek-V4.1 Flash succeeded on 17 of 18 runs; Bitdefender blocked all but 5 of 15.
On a hard subset, Claude Opus 5.5-authored workflows substantially improved weaker executors with the executor held fixed.
The best system found 71 errors and the pooled systems found 93; seven missed errors were all omissions.
It scores 54.0 overall versus 47.9 for AI Scientist-v2, and authors report chemistry yield gains in collaborations.
On a Groq-hosted qwen3-32b router, efficiency-first routing had a 29.2% shadow-invocation rate versus 4.2% when policy-aware.
Across 150 runs and over 10,000 GPU-hours, 135 still improved in the second half of a 24-hour budget.
Four LLMs show persona-driven shifts that often fail to become stable, context-sensitive policies over multi-month financial trajectories.
On a 300-part MHCAD benchmark, the training-free pipeline reaches 82.7% Structural Yield.
The benchmark covers 36 tasks in five scenarios, including indirect prompt injection that can move from digital resources into physical actions.
Agentic tool use mainly helps perception-heavy tasks; localization and expert-style assessment stay weak.

Co-Design Bench shows top models like GPT-6 Astra complete only 26% of real tasks, requiring human cleanup.
The benchmark has 1,728 scenarios and 198 image attacks; a Meta-Auditor reached 98.5% recall at 2.9% false positives.

Mutations to DeepSWE tasks cause large performance drops in models due to benchmark overfitting.

Of 135 submissions from nine agents, 114 failed integrity checks, and only GPT-6 Astra produced one complete paper.
Pass@1 across 47 configurations ranged from 6.0% to 67.7%, and verifiers agreed with humans 93.3% of the time.
Benchmark shows frontier LLMs drift from authorization boundaries in 43% to 61% of tasks, but clean-success paths exist for 86% of cases.
Twelve agents mostly finish analyses, but half of correct answers follow an altered satellite when the ground is unchanged.
GPT-6.1-sol leads object and scene levels on Frontier3DBench while reference reconstruction remains a key bottleneck
Best agent passes 75% of migrations while others perform worse, showing migration remains unreliable.
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