METR

The full report has much more information than we could convey here, including details on the projects the agents collectively pursued, the technologies they developed for communication and coordination, and interactive figures analyzing agent activity: metr.org/blog/2026-08...

Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident metr.org

You can find additional information about our pre-deployment evaluation of GPT-5.6 Sol on our website: https://t.co/jvGwK5ljp7

Summary of METR's predeployment evaluation of GPT-5.6 Sol metr.org

We are significantly expanding our team and starting new ambitious projects. Join our team to help us realize this opportunity: https://t.co/8XoCmUPJiK.

Careers at METR metr.org

See our blog for more: metr.org/blog/2026-07...

How independent researchers could investigate AI propensities after misalignment incidents metr.org

See the post for more, including: (1) a sketch of what we know about AI-assisted R&D; (2) alternative metrics for optimization ability; (3) estimated returns to human labor in NanoGPT; (4) details on NanoGPT agent runs. metr.org/blog/2026-07...

Expenditure Horizon: Measuring Optimization Ability, with an Application to NanoGPT metr.org

METR and Redwood Research investigated agent behavior in the Hugging Face incident. We found agents developed a universal cheat for ExploitGym within 4 hours, then coordinated multi-day R&D efforts to trick the scorer into accepting cheats, including trying to tamper with logs.

We have reached an agreement with OpenAI to conduct an independent review, with Redwood Research, of the model behavior observed during the Hugging Face incident. We will publish a blog post that describes the terms of our engagement, the scope covered, and tentative conclusions.

OpenAI gave METR early access to GPT-5.6 Sol for testing including raw chain-of-thought, a railfree version of the model, and internal information about the model. With this access, METR conducted a pre-deployment evaluation of GPT-5.6 Sol, including an attempted measurement of its 50%-Time Horizon.

Introducing “expenditure horizon”: a proposed method for measuring AI capabilities on continuously-scored problems. The method compares performance as a function of spend for humans vs agents. The point where humans become more cost-effective is the agent’s expenditure horizon.

In the last 6 months, METR raised commitments of around $71 million. This will fund ambitious projects: studying autonomous capabilities, tracking recursive self-improvement, evaluating monitoring systems, conducting risk assessments, investigating AI incidents, and more.

We believe it's important to track and investigate misalignment incidents: cases where an AI agent autonomously took sophisticated, sustained actions in violation of human intent. In a new post, we lay out how independent propensity investigations of such incidents could be conducted.