Stanford PhD Student Steven Dillmann Introduces Terminal-Bench-Science: A Benchmark for AI Agents in Scientific Research
A talk from Frontier Data Summit 2026 by Steven Dillmann, a Stanford PhD student and Project Lead for Terminal-Bench-Science. The benchmark evaluates AI agents' capabilities in scientific research workflows across life, physical, Earth, mathematical, and engineering sciences, focusing on the scientist-led task review process and initial model results from the 0.1 release.
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This resource is a talk video from Frontier Data Summit 2026, presented by Steven Dillmann, a Stanford PhD student and Project Lead for Terminal-Bench-Science.
Terminal-Bench-Science aims to evaluate AI agents' performance in real scientific research workflows, covering five major scientific domains: life, physical, Earth, mathematical, and engineering sciences.
The video covers the scientist-led task review process, examples and results from the 0.1 release, leaderboard results across models and domains, and a roadmap for how researchers can contribute tasks.
The project received support through Snorkel AI’s Open Benchmarks Grants. For researchers and developers interested in the actual capability boundaries of AI agents in scientific discovery, this video offers direct reference material on benchmark construction methods and preliminary evaluation results.