AI Agents Hunt Errors in Materials Data

🔥 What happened Hashimoto and Tomai ran a multi-agent system through 100 analyses of thermoelectric experimental data, split across 10 themes and 10 cycles. Each agent has a fixed role, runs quality checks, and logs traceable analysis records. A specialized AI then answers materials questions from those records with cited sources. 💡 Why it matters Traceability exposed errors: an answer built on a preliminary corpus was traced back to raw data and found wrong. Traceability makes errors findable, not absent. That's the gap between a demo and a tool engineers actually trust. ⚡ Our take Finally, someone treats traceability as an error detector instead of a selling point. Anyone deploying agents in research should read this 74-page paper — before assuming their own pipeline is infallible.