{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:RXT5KNUECKHHX4SG2QYSSYIDAP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4843e1844117e1b591464fbc3a2674b629df7234cceaad7deab4c76d874b92fa","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2026-07-24T08:31:25Z","title_canon_sha256":"f1287bea766c763763984a8c96e3bb8e2e996ea443db287a8648dea254feab54"},"schema_version":"1.0","source":{"id":"2607.22082","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.22082","created_at":"2026-07-27T01:20:47Z"},{"alias_kind":"arxiv_version","alias_value":"2607.22082v1","created_at":"2026-07-27T01:20:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22082","created_at":"2026-07-27T01:20:47Z"},{"alias_kind":"pith_short_12","alias_value":"RXT5KNUECKHH","created_at":"2026-07-27T01:20:47Z"},{"alias_kind":"pith_short_16","alias_value":"RXT5KNUECKHHX4SG","created_at":"2026-07-27T01:20:47Z"},{"alias_kind":"pith_short_8","alias_value":"RXT5KNUE","created_at":"2026-07-27T01:20:47Z"}],"graph_snapshots":[{"event_id":"sha256:93879c4448f3b53e540beb7eb2c1900ba0b35d9bed0d0df63fc664f40dd5e28e","target":"graph","created_at":"2026-07-27T01:20:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.22082/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Brain network analysis is crucial for understanding cognition and neurological disorders, yet existing deep learning methods mainly treat connectome analysis as a graph-to-logit classification problem, offering limited explanatory reasoning. Large language models (LLMs) provide a promising interface for knowledge-intensive scientific analysis, but directly applying general-purpose LLMs to brain networks remains challenging due to the structure-language gap, limited neuroscience grounding, and overconfident positive predictions. In this paper, we propose \\textbf{BrainAgent}, an agentic LLM fram","authors_text":"Jiaxing Li, Muyao Tang, Rui Dong, Youyong Kong","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2026-07-24T08:31:25Z","title":"When Language Models Meet NeuroGraphs: Exploring Enhanced Agentic LLM Framework Towards Brain Network Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22082","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4df669356d763bd08125418b673f9fe457008d4c1bb7fe326bb74565f05a65f3","target":"record","created_at":"2026-07-27T01:20:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4843e1844117e1b591464fbc3a2674b629df7234cceaad7deab4c76d874b92fa","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2026-07-24T08:31:25Z","title_canon_sha256":"f1287bea766c763763984a8c96e3bb8e2e996ea443db287a8648dea254feab54"},"schema_version":"1.0","source":{"id":"2607.22082","kind":"arxiv","version":1}},"canonical_sha256":"8de7d53684128e7bf246d43129610303dcac72495f7ade5078078f554a56be71","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8de7d53684128e7bf246d43129610303dcac72495f7ade5078078f554a56be71","first_computed_at":"2026-07-27T01:20:47.125812Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-27T01:20:47.125812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"soAMoYjO3+7rF1f92qhfL/LII4RdVLzoNxBT4yV5VOpq9Lb4xeJLVtgxUzx3dSJipW3bbtpBNSjeX0WzgSDjDg==","signature_status":"signed_v1","signed_at":"2026-07-27T01:20:47.126758Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.22082","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4df669356d763bd08125418b673f9fe457008d4c1bb7fe326bb74565f05a65f3","sha256:93879c4448f3b53e540beb7eb2c1900ba0b35d9bed0d0df63fc664f40dd5e28e"],"state_sha256":"00543ba0ac6e94ad3d555437fe97737257dd760dd6c9dff6bb38becf98c2f287"}