{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4HT27YMIA3ABCVWPZJIZLJLVE3","short_pith_number":"pith:4HT27YMI","schema_version":"1.0","canonical_sha256":"e1e7afe18806c01156cfca5195a57526e2ad45872840a502f952b98c43c8f0c6","source":{"kind":"arxiv","id":"2506.19732","version":1},"attestation_state":"computed","paper":{"title":"Who Does What in Deep Learning? Multidimensional Game-Theoretic Attribution of Function of Neural Units","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Claus C. Hilgetag, Fatemeh Hadaeghi, Kayson Fakhar, Konrad P. Kording, Patrick Mineault, Shrey Dixit","submitted_at":"2025-06-24T15:50:35Z","abstract_excerpt":"Neural networks now generate text, images, and speech with billions of parameters, producing a need to know how each neural unit contributes to these high-dimensional outputs. Existing explainable-AI methods, such as SHAP, attribute importance to inputs, but cannot quantify the contributions of neural units across thousands of output pixels, tokens, or logits. Here we close that gap with Multiperturbation Shapley-value Analysis (MSA), a model-agnostic game-theoretic framework. By systematically lesioning combinations of units, MSA yields Shapley Modes, unit-wise contribution maps that share th"},"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":"2506.19732","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-24T15:50:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2c394f042ee166a43e7aedabec2af28ed9a5bc9fe4642e695d6594fa32e10917","abstract_canon_sha256":"d405f16e83d0b866cb7bea1f33ea5eb54f0a0943405663f77533e0b1bebd3b78"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:26:37.768665Z","signature_b64":"NONyKTIGdsI749xWeRWwfiyE+aVaXIMekHJgw3nRqE86iK22FbhJnuJ+PLLL54WVgKxRR0LWQTZpS75g+CAzCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1e7afe18806c01156cfca5195a57526e2ad45872840a502f952b98c43c8f0c6","last_reissued_at":"2026-07-05T11:26:37.768159Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:26:37.768159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Who Does What in Deep Learning? Multidimensional Game-Theoretic Attribution of Function of Neural Units","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Claus C. Hilgetag, Fatemeh Hadaeghi, Kayson Fakhar, Konrad P. Kording, Patrick Mineault, Shrey Dixit","submitted_at":"2025-06-24T15:50:35Z","abstract_excerpt":"Neural networks now generate text, images, and speech with billions of parameters, producing a need to know how each neural unit contributes to these high-dimensional outputs. Existing explainable-AI methods, such as SHAP, attribute importance to inputs, but cannot quantify the contributions of neural units across thousands of output pixels, tokens, or logits. Here we close that gap with Multiperturbation Shapley-value Analysis (MSA), a model-agnostic game-theoretic framework. By systematically lesioning combinations of units, MSA yields Shapley Modes, unit-wise contribution maps that share th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.19732","kind":"arxiv","version":1},"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/2506.19732/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":"2506.19732","created_at":"2026-07-05T11:26:37.768214+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.19732v1","created_at":"2026-07-05T11:26:37.768214+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.19732","created_at":"2026-07-05T11:26:37.768214+00:00"},{"alias_kind":"pith_short_12","alias_value":"4HT27YMIA3AB","created_at":"2026-07-05T11:26:37.768214+00:00"},{"alias_kind":"pith_short_16","alias_value":"4HT27YMIA3ABCVWP","created_at":"2026-07-05T11:26:37.768214+00:00"},{"alias_kind":"pith_short_8","alias_value":"4HT27YMI","created_at":"2026-07-05T11:26:37.768214+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.17958","citing_title":"VIBE: Video-Input Brain Encoder for fMRI Response Modeling","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4HT27YMIA3ABCVWPZJIZLJLVE3","json":"https://pith.science/pith/4HT27YMIA3ABCVWPZJIZLJLVE3.json","graph_json":"https://pith.science/api/pith-number/4HT27YMIA3ABCVWPZJIZLJLVE3/graph.json","events_json":"https://pith.science/api/pith-number/4HT27YMIA3ABCVWPZJIZLJLVE3/events.json","paper":"https://pith.science/paper/4HT27YMI"},"agent_actions":{"view_html":"https://pith.science/pith/4HT27YMIA3ABCVWPZJIZLJLVE3","download_json":"https://pith.science/pith/4HT27YMIA3ABCVWPZJIZLJLVE3.json","view_paper":"https://pith.science/paper/4HT27YMI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.19732&json=true","fetch_graph":"https://pith.science/api/pith-number/4HT27YMIA3ABCVWPZJIZLJLVE3/graph.json","fetch_events":"https://pith.science/api/pith-number/4HT27YMIA3ABCVWPZJIZLJLVE3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4HT27YMIA3ABCVWPZJIZLJLVE3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4HT27YMIA3ABCVWPZJIZLJLVE3/action/storage_attestation","attest_author":"https://pith.science/pith/4HT27YMIA3ABCVWPZJIZLJLVE3/action/author_attestation","sign_citation":"https://pith.science/pith/4HT27YMIA3ABCVWPZJIZLJLVE3/action/citation_signature","submit_replication":"https://pith.science/pith/4HT27YMIA3ABCVWPZJIZLJLVE3/action/replication_record"}},"created_at":"2026-07-05T11:26:37.768214+00:00","updated_at":"2026-07-05T11:26:37.768214+00:00"}