Autonomous Science Bench: Evaluating AI Agents for Autonomous Scientific Discovery
- Published
- Source
- arXiv
- Paper number
- 1209
- Field
- AI / Agents
- arXiv ID
- 2610.18449
Key points
- Autonomous Science Bench is a science-agent benchmark comprising six budgeted design-build-test-learn campaigns grounded in published experiments across biology, chemistry, and materials science.
- It evaluates an active experimentation loop where agents select design batches, receive measurements, and plan subsequent experiments before delivering a predictive model.
- In a pilot of eight agent-model configurations and 144 trials, the best configuration succeeded in 67% of trials and the hardest campaign was solved in only three of 24 attempts.
- Budgets cover at most 11% of each candidate pool, so covering the pool evenly is not enough to pass.
- Opus 5.5 in Claude Code resolved 67% of campaign trials, with GPT-5.6 Terra and GPT-5.6 Luna following at 44% and 28%.
Paper links
External research summaries. These are not HDATF publications or measured product results.