{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XBECD4FQZYKHHJSJDQPVL5E6WD","short_pith_number":"pith:XBECD4FQ","schema_version":"1.0","canonical_sha256":"b84821f0b0ce1473a6491c1f55f49eb0f390601a9fe2ea86d214ff324e5dcc62","source":{"kind":"arxiv","id":"2503.09046","version":2},"attestation_state":"computed","paper":{"title":"Discovering Influential Neuron Path in Vision Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Anqi Pang, Changming Li, Jingyi Yu, Kan Ren, Sibei Yang, Yifan Wang, Yifei Liu, Yingdong Shi","submitted_at":"2025-03-12T04:10:46Z","abstract_excerpt":"Vision Transformer models exhibit immense power yet remain opaque to human understanding, posing challenges and risks for practical applications. While prior research has attempted to demystify these models through input attribution and neuron role analysis, there's been a notable gap in considering layer-level information and the holistic path of information flow across layers. In this paper, we investigate the significance of influential neuron paths within vision Transformers, which is a path of neurons from the model input to output that impacts the model inference most significantly. We f"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2503.09046","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-03-12T04:10:46Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"b307a2f6dc76641019d85b3c954171b2b9ad37b4b3ff6edde5872d2562c4fca1","abstract_canon_sha256":"8e8f01eedddc1fec0e8e5865ded577f74d9b754417044c1455915d7901c03a6b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:23.140190Z","signature_b64":"kwf36ut9ot3dgCReTwz5TX32boPiJJzpAUNeMOd4axwcAxFO3MvgsNsB4bffRvp+reBAKiHUFKxLc9PR656RAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b84821f0b0ce1473a6491c1f55f49eb0f390601a9fe2ea86d214ff324e5dcc62","last_reissued_at":"2026-07-05T10:46:23.139734Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:23.139734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Discovering Influential Neuron Path in Vision Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Anqi Pang, Changming Li, Jingyi Yu, Kan Ren, Sibei Yang, Yifan Wang, Yifei Liu, Yingdong Shi","submitted_at":"2025-03-12T04:10:46Z","abstract_excerpt":"Vision Transformer models exhibit immense power yet remain opaque to human understanding, posing challenges and risks for practical applications. While prior research has attempted to demystify these models through input attribution and neuron role analysis, there's been a notable gap in considering layer-level information and the holistic path of information flow across layers. In this paper, we investigate the significance of influential neuron paths within vision Transformers, which is a path of neurons from the model input to output that impacts the model inference most significantly. We f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.09046","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2503.09046/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2503.09046","created_at":"2026-07-05T10:46:23.139792+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.09046v2","created_at":"2026-07-05T10:46:23.139792+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.09046","created_at":"2026-07-05T10:46:23.139792+00:00"},{"alias_kind":"pith_short_12","alias_value":"XBECD4FQZYKH","created_at":"2026-07-05T10:46:23.139792+00:00"},{"alias_kind":"pith_short_16","alias_value":"XBECD4FQZYKHHJSJ","created_at":"2026-07-05T10:46:23.139792+00:00"},{"alias_kind":"pith_short_8","alias_value":"XBECD4FQ","created_at":"2026-07-05T10:46:23.139792+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.01728","citing_title":"Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XBECD4FQZYKHHJSJDQPVL5E6WD","json":"https://pith.science/pith/XBECD4FQZYKHHJSJDQPVL5E6WD.json","graph_json":"https://pith.science/api/pith-number/XBECD4FQZYKHHJSJDQPVL5E6WD/graph.json","events_json":"https://pith.science/api/pith-number/XBECD4FQZYKHHJSJDQPVL5E6WD/events.json","paper":"https://pith.science/paper/XBECD4FQ"},"agent_actions":{"view_html":"https://pith.science/pith/XBECD4FQZYKHHJSJDQPVL5E6WD","download_json":"https://pith.science/pith/XBECD4FQZYKHHJSJDQPVL5E6WD.json","view_paper":"https://pith.science/paper/XBECD4FQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.09046&json=true","fetch_graph":"https://pith.science/api/pith-number/XBECD4FQZYKHHJSJDQPVL5E6WD/graph.json","fetch_events":"https://pith.science/api/pith-number/XBECD4FQZYKHHJSJDQPVL5E6WD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XBECD4FQZYKHHJSJDQPVL5E6WD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XBECD4FQZYKHHJSJDQPVL5E6WD/action/storage_attestation","attest_author":"https://pith.science/pith/XBECD4FQZYKHHJSJDQPVL5E6WD/action/author_attestation","sign_citation":"https://pith.science/pith/XBECD4FQZYKHHJSJDQPVL5E6WD/action/citation_signature","submit_replication":"https://pith.science/pith/XBECD4FQZYKHHJSJDQPVL5E6WD/action/replication_record"}},"created_at":"2026-07-05T10:46:23.139792+00:00","updated_at":"2026-07-05T10:46:23.139792+00:00"}