BrainPilot: Automating Brain Discovery with Agentic Research

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Haoxuan Li, Tianci Gao, Jianhe Li, Yang Fan, Runze Shi, Weiran Wang, Tianxiang Zhao, Zezhao Wu, Xiaoyang Jiang, Qihui Zhang, Jia Li, Xiao Xiao, Kai Du, Xiaoxuan Jia, Chao Xie, Lu Mi
• Venue: arXiv: cs.AI
• Published: 2026-07-16
• Collected: 2026-07-17
• URL: http://arxiv.org/abs/2607.15079v1
• PDF: https://arxiv.org/pdf/2607.15079v1
• DOI: UNKNOWN

Plain-Language Brief

BrainPilot is a multi-agent system designed to accelerate brain science research by integrating evidence from various scales, modalities, and disciplines. The system uses a principal investigator (PI) agent and specialist agents grounded in curated domain knowledge to perform tasks, record every major step, and integrate fabrication checking into the workflow. BrainPilot has shown comparable performance to state-of-the-art agent frameworks with less costs.

What the Document Says

• BrainPilot is a multi-agent system for brain science research.
• It uses a PI agent and specialist agents grounded in curated domain knowledge.
• The system has a unified brain science knowledge base with 7233 indexed items.
• There is a skill library of 72 reusable methodology units across seven research domains.
• Every major step is recorded in the Graph of Trace, an auditable record that links subgoals, tool use, evidence, and claims.
• An Auditor agent checks for fabrication within the workflow.
• The system has been evaluated through three brain science tasks from Agents' Last Exam, a new benchmark (BrainPilotBench-v0), and end-to-end case studies.
• With an open-source backbone model, BrainPilot shows comparable performance to state-of-the-art agent frameworks with less costs.

Key Concepts Mentioned

• BrainPilot
• multi-agent system
• brain science research
• PI agent
• specialist agents
• unified brain science knowledge base
• Graph of Trace
• Auditor agent
• fabrication checking
• Agents' Last Exam
• BrainPilotBench-v0

Claims Made by the Document

• BrainPilot can accelerate brain science research.
• BrainPilot with an open-source backbone model attains performance comparable to state-of-the-art agent frameworks.

Evidence Used

• The text describes the purpose and design of BrainPilot, its components, and its evaluation.

Methods / Approach

• Using a multi-agent system for brain science research.
• Recording every major step in an auditable record.
• Incorporating fabrication checking into the workflow.

Limitations Stated by the Authors

The authors mention that current AI agents lack domain expertise in brain science and may have issues with fabricating claims, drifting during multi-step reasoning, and offering few defined points for expert intervention.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The limitations of the agent are not explicitly stated in the text.

Open Questions

• What specific tasks were performed in the evaluations?
• What was the nature of the end-to-end case studies?

Notes for Future Q&A

May include interpretation. Labeled where it does.

It would be useful to know more details about the evaluations and case studies mentioned in the text.

Raw Abstract / Excerpt

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 offer few defined points for expert intervention. These failures are especially costly in brain science, where conclusions feed into downstream scientific claims and depend on laboratory-specific expertise and careful human judgment. We present \textbf{BrainPilot} a \textbf{fully open-source} multi-agent system that accelerates brain science research with traceable logs and agent-verified results. A principal investigator (PI) agent coordinates specialist agents grounded in curated domain knowledge: a unified brain science knowledge base containing 7{,}233 indexed items and a skill library of 72 reusable methodology units across seven research domains. Every major step is recorded in the Graph of Trace, an auditable record that links subgoals, tool use, evidence, and claims and allows researchers to follow and inspect the workflow. An Auditor agent further integrates fabrication checking into the workflow. For evaluation, we run three brain science tasks from Agents' Last Exam, introduce our own benchmark, \textbf{BrainPilotBench-v0}, and present additional end-to-end case studies. Across these evaluations, BrainPilot with an open-source backbone model attains performance comparable to state-of-the-art agent framework with less costs.