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. It uses a unified brain science knowledge base containing over 7000 items and a skill library of 72 reusable methodology units across seven research domains. The system records every major step in an auditable Graph of Trace, allowing researchers to follow and inspect the workflow. An Auditor agent checks for fabrication during the process. In evaluations, BrainPilot performed comparably to state-of-the-art agent frameworks with less cost.

What the Document Says

• BrainPilot is a multi-agent system
• It is designed for brain science research
• Uses a unified brain science knowledge base with 7233 indexed items
• Has a skill library of 72 reusable methodology units across seven research domains
• Records every major step in an auditable Graph of Trace
• Includes an Auditor agent for fabrication checking
• Performs evaluations on brain science tasks from Agents' Last Exam, BrainPilotBench-v0, and case studies

Key Concepts Mentioned

• BrainPilot
• Multi-agent system
• Brain science research
• Unified brain science knowledge base
• Skill library
• Graph of Trace
• Auditor agent
• Fabrication checking
• Agents' Last Exam
• BrainPilotBench-v0

Claims Made by the Document

• BrainPilot accelerates brain science research
• BrainPilot attains performance comparable to state-of-the-art agent frameworks with less cost

Evidence Used

• Description of BrainPilot's functionality and design
• Results from evaluations and case studies

Methods / Approach

• Use of a unified brain science knowledge base
• Recording steps in an auditable Graph of Trace
• Fabrication checking by Auditor agent

Limitations Stated by the Authors

• Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

• The paper does not provide details about the specific challenges faced during the development and testing of BrainPilot or its performance in other areas outside brain science research.

Open Questions

• What are the specific challenges faced during the development and testing of BrainPilot?
• How does BrainPilot perform in other areas outside brain science research?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The paper focuses on evaluating BrainPilot specifically for brain science research, so it is unclear how well it might perform in other scientific domains.

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.