Haoxuan Li, Tianci Gao, Jianhe Li +9 more
Brain research is a coordination problem as much as a science problem: any single question can mean surveying literature, running analyses, and reading the results against messy domain knowledge. Off-the-shelf AI agents can do some of that, but they wander over long reasoning chains, invent citations, and give experts nowhere obvious to step in.
BrainPilot is an open-source attempt to make agents behave in this setting. A principal investigator agent hands work to specialists drawn from a curated knowledge base of 7,233 items and 72 reusable methodology units across seven domains. Every step is logged in a Graph of Trace that links subgoals, tools, evidence, and claims, and a separate Auditor agent checks for fabrication. On tasks from Agents' Last Exam plus the team's own benchmark, it reportedly matches stronger commercial frameworks at lower cost.
The benchmarks and case studies here are early, so read the paper before trusting it anywhere near real lab decisions.
Understanding the brain increasingly depends on integrating evidence across scales, modalities, and disciplines. Addressing a single research question therefore requires a coordinated sequence of operations, from surveying prior work to executing analyses and interpreting results in light of domain knowledge. AI agents promise to accelerate this process, but current agents lack domain expertise in brain science, may fabricate claims, drift during multi-step reasoning, and…
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