Pith Number

pith:ZQROUJKI

pith:2026:ZQROUJKI5FYBNGJZIZDFY6N46D

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Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration

Carl Yang, Keqi Han, Lifang He, Songlin Zhao, Xiang Li, Yao Su, Yixuan Yuan

Multi-agent system NIAgent autonomously builds and refines neuroimaging analysis workflows to outperform fixed pipelines.

arxiv:2605.09366 v2 · 2026-05-10 · cs.AI

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\pithnumber{ZQROUJKI5FYBNGJZIZDFY6N46D}

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Experiments on ADHD-200 and ADNI demonstrate that NIAgent outperforms standard workflow-based baselines in predictive performance while exhibiting sophisticated agentic behaviors, including strategy exploration and adaptive refinement.

C2weakest assumption

The hierarchical verification framework integrating cohort-level metric screening with agentic visual inspection is sufficient to drive evidence-grounded workflow remediation without human intervention or additional safeguards.

C3one line summary

NIAgent is a multi-agent system using code-centric execution and hierarchical verification to autonomously build and adapt neuroimaging analysis workflows, showing better predictive performance than standard pipelines on ADHD-200 and ADNI data.

References

55 extracted · 55 resolved · 4 Pith anchors

[1] The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.Scientific data, 3(1):1–9 2016

[2] fmriprep: a robust preprocessing pipeline for functional mri.Nature methods, 16(1):111–116 2019

[3] A comprehensive overview of large language models.ACM Transactions on Intelligent Systems and Technology, 16(5):1–72 2025

[4] Agentic ai for scientific discovery: A survey of progress, challenges, and future directions 2025

[5] The adhd-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience.Frontiers in systems neuroscience, 6:62 2012

Formal links

2 machine-checked theorem links

Cited by

1 paper in Pith

EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs

Receipt and verification

First computed	2026-05-20T00:00:41.932312Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

cc22ea2548e97016993946465c79bcf0c5b822db2158d75822a69703f16e49e2

Aliases

arxiv: 2605.09366 · arxiv_version: 2605.09366v2 · doi: 10.48550/arxiv.2605.09366 · pith_short_12: ZQROUJKI5FYB · pith_short_16: ZQROUJKI5FYBNGJZ · pith_short_8: ZQROUJKI

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZQROUJKI5FYBNGJZIZDFY6N46D \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: cc22ea2548e97016993946465c79bcf0c5b822db2158d75822a69703f16e49e2

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e79e683f8ef0f45cc5eaf81d782a287769e18379ef2d9ad01123fedd8d00f7ed",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-10T06:30:19Z",
    "title_canon_sha256": "95ff0588860c11030f4ae43ab0aa4c6b17bdf5d3575361c690101231c83f5996"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.09366",
    "kind": "arxiv",
    "version": 2
  }
}