{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:GMPGDVFXVI2JIB2EJEAJUKBS3Y","short_pith_number":"pith:GMPGDVFX","canonical_record":{"source":{"id":"2311.12786","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-21T18:51:04Z","cross_cats_sorted":[],"title_canon_sha256":"9cb9856c1ee60a45022fa279ec469a4740941eb6ff235e98ab7297c55e34fe4e","abstract_canon_sha256":"78caa8c15e7c1f4595f911bdb3f70fbbff9199743d7b2659a39d87d998c65f09"},"schema_version":"1.0"},"canonical_sha256":"331e61d4b7aa3494074449009a2832de2a6f3c6c1cf7eb2a95449e530264d7ff","source":{"kind":"arxiv","id":"2311.12786","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.12786","created_at":"2026-07-05T08:57:34Z"},{"alias_kind":"arxiv_version","alias_value":"2311.12786v2","created_at":"2026-07-05T08:57:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12786","created_at":"2026-07-05T08:57:34Z"},{"alias_kind":"pith_short_12","alias_value":"GMPGDVFXVI2J","created_at":"2026-07-05T08:57:34Z"},{"alias_kind":"pith_short_16","alias_value":"GMPGDVFXVI2JIB2E","created_at":"2026-07-05T08:57:34Z"},{"alias_kind":"pith_short_8","alias_value":"GMPGDVFX","created_at":"2026-07-05T08:57:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:GMPGDVFXVI2JIB2EJEAJUKBS3Y","target":"record","payload":{"canonical_record":{"source":{"id":"2311.12786","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-21T18:51:04Z","cross_cats_sorted":[],"title_canon_sha256":"9cb9856c1ee60a45022fa279ec469a4740941eb6ff235e98ab7297c55e34fe4e","abstract_canon_sha256":"78caa8c15e7c1f4595f911bdb3f70fbbff9199743d7b2659a39d87d998c65f09"},"schema_version":"1.0"},"canonical_sha256":"331e61d4b7aa3494074449009a2832de2a6f3c6c1cf7eb2a95449e530264d7ff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:34.827754Z","signature_b64":"ZCZkIWOM9aVGDdM03sBZbsNdbQRmYhdTMlFmF9EcmvUVx+L9/+6SRKiHGh9PRWnO0sTLvwmrv13TFaGOQu6bBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"331e61d4b7aa3494074449009a2832de2a6f3c6c1cf7eb2a95449e530264d7ff","last_reissued_at":"2026-07-05T08:57:34.827245Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:34.827245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.12786","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:57:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kc2h6xs9jt8ctZWm9ImtdIyN5yJnRqGbEEajDjzaw4XGC649IigCTCa7jD8knnyS5MjY6htd4D3rieYVpn/HBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T16:28:43.212093Z"},"content_sha256":"5d257978e6fe8e850012fa3adccc93dfb4a983dc72bf36bc5fae5aa6aa5cf359","schema_version":"1.0","event_id":"sha256:5d257978e6fe8e850012fa3adccc93dfb4a983dc72bf36bc5fae5aa6aa5cf359"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:GMPGDVFXVI2JIB2EJEAJUKBS3Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"David Scott Krueger, Edward Grefenstette, Ekdeep Singh Lubana, Hidenori Tanaka, Robert Kirk, Robert P. Dick, Samyak Jain, Tim Rockt\\\"aschel","submitted_at":"2023-11-21T18:51:04Z","abstract_excerpt":"Fine-tuning large pre-trained models has become the de facto strategy for developing both task-specific and general-purpose machine learning systems, including developing models that are safe to deploy. Despite its clear importance, there has been minimal work that explains how fine-tuning alters the underlying capabilities learned by a model during pretraining: does fine-tuning yield entirely novel capabilities or does it just modulate existing ones? We address this question empirically in synthetic, controlled settings where we can use mechanistic interpretability tools (e.g., network prunin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12786","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/2311.12786/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:57:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dzCmYOnJYs5FISFy2PwZC9qfAcDX1dvRt/UdWK8wDyHOC5koR+FLBx81abDlTuVMzJWwP1b2Bi4QWkgAAaubAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T16:28:43.212426Z"},"content_sha256":"3db22a0b0767f1c1f921c1d97d0b7b844ae6e0d756835f56d357db72a4a27c17","schema_version":"1.0","event_id":"sha256:3db22a0b0767f1c1f921c1d97d0b7b844ae6e0d756835f56d357db72a4a27c17"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GMPGDVFXVI2JIB2EJEAJUKBS3Y/bundle.json","state_url":"https://pith.science/pith/GMPGDVFXVI2JIB2EJEAJUKBS3Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GMPGDVFXVI2JIB2EJEAJUKBS3Y/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-19T16:28:43Z","links":{"resolver":"https://pith.science/pith/GMPGDVFXVI2JIB2EJEAJUKBS3Y","bundle":"https://pith.science/pith/GMPGDVFXVI2JIB2EJEAJUKBS3Y/bundle.json","state":"https://pith.science/pith/GMPGDVFXVI2JIB2EJEAJUKBS3Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GMPGDVFXVI2JIB2EJEAJUKBS3Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GMPGDVFXVI2JIB2EJEAJUKBS3Y","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":"78caa8c15e7c1f4595f911bdb3f70fbbff9199743d7b2659a39d87d998c65f09","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-21T18:51:04Z","title_canon_sha256":"9cb9856c1ee60a45022fa279ec469a4740941eb6ff235e98ab7297c55e34fe4e"},"schema_version":"1.0","source":{"id":"2311.12786","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.12786","created_at":"2026-07-05T08:57:34Z"},{"alias_kind":"arxiv_version","alias_value":"2311.12786v2","created_at":"2026-07-05T08:57:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12786","created_at":"2026-07-05T08:57:34Z"},{"alias_kind":"pith_short_12","alias_value":"GMPGDVFXVI2J","created_at":"2026-07-05T08:57:34Z"},{"alias_kind":"pith_short_16","alias_value":"GMPGDVFXVI2JIB2E","created_at":"2026-07-05T08:57:34Z"},{"alias_kind":"pith_short_8","alias_value":"GMPGDVFX","created_at":"2026-07-05T08:57:34Z"}],"graph_snapshots":[{"event_id":"sha256:3db22a0b0767f1c1f921c1d97d0b7b844ae6e0d756835f56d357db72a4a27c17","target":"graph","created_at":"2026-07-05T08:57:34Z","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/2311.12786/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning large pre-trained models has become the de facto strategy for developing both task-specific and general-purpose machine learning systems, including developing models that are safe to deploy. Despite its clear importance, there has been minimal work that explains how fine-tuning alters the underlying capabilities learned by a model during pretraining: does fine-tuning yield entirely novel capabilities or does it just modulate existing ones? We address this question empirically in synthetic, controlled settings where we can use mechanistic interpretability tools (e.g., network prunin","authors_text":"David Scott Krueger, Edward Grefenstette, Ekdeep Singh Lubana, Hidenori Tanaka, Robert Kirk, Robert P. Dick, Samyak Jain, Tim Rockt\\\"aschel","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-21T18:51:04Z","title":"Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12786","kind":"arxiv","version":2},"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":"sha256:5d257978e6fe8e850012fa3adccc93dfb4a983dc72bf36bc5fae5aa6aa5cf359","target":"record","created_at":"2026-07-05T08:57:34Z","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":"78caa8c15e7c1f4595f911bdb3f70fbbff9199743d7b2659a39d87d998c65f09","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-21T18:51:04Z","title_canon_sha256":"9cb9856c1ee60a45022fa279ec469a4740941eb6ff235e98ab7297c55e34fe4e"},"schema_version":"1.0","source":{"id":"2311.12786","kind":"arxiv","version":2}},"canonical_sha256":"331e61d4b7aa3494074449009a2832de2a6f3c6c1cf7eb2a95449e530264d7ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"331e61d4b7aa3494074449009a2832de2a6f3c6c1cf7eb2a95449e530264d7ff","first_computed_at":"2026-07-05T08:57:34.827245Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:57:34.827245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZCZkIWOM9aVGDdM03sBZbsNdbQRmYhdTMlFmF9EcmvUVx+L9/+6SRKiHGh9PRWnO0sTLvwmrv13TFaGOQu6bBg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:57:34.827754Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.12786","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5d257978e6fe8e850012fa3adccc93dfb4a983dc72bf36bc5fae5aa6aa5cf359","sha256:3db22a0b0767f1c1f921c1d97d0b7b844ae6e0d756835f56d357db72a4a27c17"],"state_sha256":"53a000d13931a92e1f373abc03706a4b25234e564124d5ed01eece59a967015c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AKg3kJxwvAhAhVdtsAlC6YLfWo8Y8Ztd02EIFiY6v7EGZ+DnijhC+VaZotmJ68GWEpGc+36OBe1Ll74LC8FvAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T16:28:43.215846Z","bundle_sha256":"e3f2bf8a99f93d6f9d885959d93f9c60e7aafee739d31e9456ce88845b74012d"}}