{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:T3KYSRC2QHLQCI4WX23YUJRKB7","short_pith_number":"pith:T3KYSRC2","schema_version":"1.0","canonical_sha256":"9ed589445a81d7012396beb78a262a0fe7136c017daeb482a8fc4025cc174916","source":{"kind":"arxiv","id":"2404.03646","version":2},"attestation_state":"computed","paper":{"title":"Locating and Editing Factual Associations in Mamba","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arnab Sen Sharma, David Atkinson, David Bau","submitted_at":"2024-04-04T17:58:31Z","abstract_excerpt":"We investigate the mechanisms of factual recall in the Mamba state space model. Our work is inspired by previous findings in autoregressive transformer language models suggesting that their knowledge recall is localized to particular modules at specific token locations; we therefore ask whether factual recall in Mamba can be similarly localized. To investigate this, we conduct four lines of experiments on Mamba. First, we apply causal tracing or interchange interventions to localize key components inside Mamba that are responsible for recalling facts, revealing that specific components within "},"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":"2404.03646","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-04T17:58:31Z","cross_cats_sorted":[],"title_canon_sha256":"78c5df23935009875eb5de865d3df84d9d3a96507554b4a5783b16e8548d5891","abstract_canon_sha256":"9828e4480d38e85400d8015a2917a51a059e0b664196af5c47c893d429e2e942"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:51:50.006503Z","signature_b64":"NtLms3Jzq/KJwW4KCA5olfus/L0NkfFByEOhdfpRvczU42pH3+WeCJtPABzPJow6IIggukm4h8W36yldoeIrAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ed589445a81d7012396beb78a262a0fe7136c017daeb482a8fc4025cc174916","last_reissued_at":"2026-07-05T08:51:50.006049Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:51:50.006049Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Locating and Editing Factual Associations in Mamba","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arnab Sen Sharma, David Atkinson, David Bau","submitted_at":"2024-04-04T17:58:31Z","abstract_excerpt":"We investigate the mechanisms of factual recall in the Mamba state space model. Our work is inspired by previous findings in autoregressive transformer language models suggesting that their knowledge recall is localized to particular modules at specific token locations; we therefore ask whether factual recall in Mamba can be similarly localized. To investigate this, we conduct four lines of experiments on Mamba. First, we apply causal tracing or interchange interventions to localize key components inside Mamba that are responsible for recalling facts, revealing that specific components within "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.03646","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/2404.03646/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":"2404.03646","created_at":"2026-07-05T08:51:50.006113+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.03646v2","created_at":"2026-07-05T08:51:50.006113+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.03646","created_at":"2026-07-05T08:51:50.006113+00:00"},{"alias_kind":"pith_short_12","alias_value":"T3KYSRC2QHLQ","created_at":"2026-07-05T08:51:50.006113+00:00"},{"alias_kind":"pith_short_16","alias_value":"T3KYSRC2QHLQCI4W","created_at":"2026-07-05T08:51:50.006113+00:00"},{"alias_kind":"pith_short_8","alias_value":"T3KYSRC2","created_at":"2026-07-05T08:51:50.006113+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2601.01972","citing_title":"Hidden State Poisoning Attacks against Mamba-based Language Models","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19089","citing_title":"Towards Scalable Lifelong Knowledge Editing with Selective Knowledge Suppression","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T3KYSRC2QHLQCI4WX23YUJRKB7","json":"https://pith.science/pith/T3KYSRC2QHLQCI4WX23YUJRKB7.json","graph_json":"https://pith.science/api/pith-number/T3KYSRC2QHLQCI4WX23YUJRKB7/graph.json","events_json":"https://pith.science/api/pith-number/T3KYSRC2QHLQCI4WX23YUJRKB7/events.json","paper":"https://pith.science/paper/T3KYSRC2"},"agent_actions":{"view_html":"https://pith.science/pith/T3KYSRC2QHLQCI4WX23YUJRKB7","download_json":"https://pith.science/pith/T3KYSRC2QHLQCI4WX23YUJRKB7.json","view_paper":"https://pith.science/paper/T3KYSRC2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.03646&json=true","fetch_graph":"https://pith.science/api/pith-number/T3KYSRC2QHLQCI4WX23YUJRKB7/graph.json","fetch_events":"https://pith.science/api/pith-number/T3KYSRC2QHLQCI4WX23YUJRKB7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T3KYSRC2QHLQCI4WX23YUJRKB7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T3KYSRC2QHLQCI4WX23YUJRKB7/action/storage_attestation","attest_author":"https://pith.science/pith/T3KYSRC2QHLQCI4WX23YUJRKB7/action/author_attestation","sign_citation":"https://pith.science/pith/T3KYSRC2QHLQCI4WX23YUJRKB7/action/citation_signature","submit_replication":"https://pith.science/pith/T3KYSRC2QHLQCI4WX23YUJRKB7/action/replication_record"}},"created_at":"2026-07-05T08:51:50.006113+00:00","updated_at":"2026-07-05T08:51:50.006113+00:00"}