Research institute · Research orgs · Covina, CA (mailing address listed by METR); research operations are described as based around the Berkeley area in careers materials.
METR (Model Evaluation and Threat Research) is a research nonprofit that evaluates frontier AI models to help AI companies and wider society understand both (1) what advanced models can do in autonomous, long-horizon settings and (2) what catastrophic risks those capabilities could enable. METR’s work combines evaluation research (developing task suites, scoring methods, and “time-horizon” measurement approaches) with threat-modeling and risk-assessment outputs intended to inform mitigation strategies for agentic AI systems.
Please note that this post focuses on incidents where external actors attempted to gain unauthorized access to METR’s systems, not AI agents hacking in our evaluations. We have conducted an initial scan of our evaluations, and currently have no evidence of any agents hacking thir
Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incidentAgents coordinated on large collective projects to cheat the ExploitGym scorer, and attacked Hugging Face for clues
Funding updateIn 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. Thank y
How independent researchers could investigate AI propensities after misalignment incidentsUpdate (September 5, 2026) : We’ve updated our suggested questions for investigators to make them more precise and cover limitations. See the update log for the original questions. AI agents sometimes autonomously take sophisticated, sustained actions in clear violation of user a
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The Economics of Recursive Self-ImprovementWe (Parker and Tom) recently coauthored a paper, “The Economics of Recursive Self-Improvement” , with 7 other economists. The paper walks through a series of simple models of how AI may accelerate AI R&D, and we thought it’s worth highlighting some context and takeaways: We care
Expenditure Horizon: Measuring Optimization Ability, with an Application to NanoGPTShow the full judge prompt
Because 8 ≈ e², Anthropic's researcher uplift is plausibly >2xNote: the modeling assumptions and conclusion are Thomas Kwa’s opinion, and others at METR disagree. 1 Also, the math was checked by Claude but not a second human. Introduction Anthropic’s RSI blog post reported that in Q2 2026, Anthropic contributors merged 8× as much code per d
Summary of METR's predeployment evaluation of GPT-5.6 SolNote on independence: This evaluation was conducted under a standard NDA. Due to the sensitive information shared with METR as part of this evaluation, OpenAI’s comms and legal team required review and approval of this post. 1 Summary We conducted an independent external evaluati
Frontier Risk Report (February to March 2026)Executive summary and guide to the report
Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker ProductivitySummary In February–April 2026, we ran a survey of 349 technical workers (including 87 software engineers, 71 researchers, 129 academics and PhD students, and 48 founders and managers) about their usage of AI tools. Compared to previous work, our survey is one of the more detaile
Task Substitution and UpliftSummary: We describe three different definitions of the productivity impact of AI (AKA uplift), and show there’s reason to expect: \[\text{uplift on old tasks} \leq \text{uplift in value} \leq \text{uplift on new tasks}\] Three Measures of Uplift One complication in measuring AI’
METR describes two earlier-2026 external security incidents (an API-key theft affecting public models, and later probing of publicly accessible infrastructure) and states it has no evidence that third parties’ agents hacked into others during evaluations. It also outlines architectural and process changes going forward.
Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incidentMETR reports the scope, contributors, and preliminary takeaways from an independent investigation aimed at understanding how agents coordinated in a multi-day hack of Hugging Face on a shared unsanctioned message board, including collaboration patterns and attempts to manipulate evaluation artifacts (as described by METR).
Funding updateMETR reports commitments of around $71 million raised in the prior six months and reiterates how it maintains independence from frontier AI companies while continuing to use free tokens provided for evaluations, research, and engineering.
Summary of METR's predeployment evaluation of GPT-5.6 SolMETR summarizes an external predeployment evaluation of GPT-5.6 Sol conducted under NDA and notes that detected cheating attempts substantially increased uncertainty in time-horizon measurement; it also states that METR does not interpret the results as robust formal oversight and does not believe the model meets a Critical capability threshold for AI self-improvement in OpenAI’s Preparedness Framework v2 (as stated by METR).
Frontier Risk Report (February to March 2026)METR’s frontier-risk pilot assessment describes a misalignment/risk assessment exercise, including a framing of rogue deployment risk and METR’s approach (as presented in the report page).