Dioptra is a volunteer AI safety research community founded by Joshua Clymer that builds evaluations for advanced AI systems.
Dioptra is a volunteer AI safety research community founded by Joshua Clymer that builds evaluations for advanced AI systems.
People– no linked people
Updated 03/21/26Funding Details
Updated 03/21/26- Annual Budget
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- Current Runway
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- Funding Goal
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- Funding Raised to Date
- $9,072
Org Details
Updated 03/21/26Dioptra is an informal volunteer AI safety research community founded and led by Joshua Clymer. Clymer is a technical AI safety researcher who has worked at METR (where he researched AI threat models and developed evaluations for self-improvement capabilities) and Redwood Research (where he specializes in safety evaluation methodologies for advanced AI agents). He is also associated with the Cambridge Boston Alignment Initiative and Pivotal Research. The group is composed of approximately 21 volunteer students and engineers, making it a lean, community-driven research effort rather than a formal institution. Dioptra's primary focus is building evaluations for AI safety — tools and benchmarks designed to measure AI capabilities, behaviors, and potential risks. One notable output associated with Dioptra is the GameBench paper, a cross-domain benchmark for evaluating the strategic reasoning abilities of large language model agents across multiple game environments. This work was accepted to the NeurIPS 2024 Language Gamification Workshop. The EA Funds Long-Term Future Fund awarded Dioptra $9,072 in Q2 2024 as retroactive funding for this paper. Dioptra operates without a dedicated public website and does not appear to be a registered nonprofit. It functions as a research community and volunteer coordination structure around Clymer's leadership, contributing to the broader AI safety evaluations ecosystem.
Theory of Change
Updated 03/21/26Dioptra aims to reduce risks from advanced AI by developing rigorous evaluations that can measure AI capabilities and behaviors, particularly those relevant to safety. By producing benchmarks and evals, the group contributes to the foundation needed for credible safety cases — structured arguments that AI systems are unlikely to cause catastrophic outcomes. Better evaluations enable developers, policymakers, and oversight bodies to make more informed decisions about AI deployment and control, thereby reducing the probability of catastrophic failures from advanced AI systems.
Grants Received
Updated 03/21/26Projects– no linked projects
Updated 03/21/26Discussion
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