{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:PNUYYMIK4M55GWW6A7DFNCTEXN","short_pith_number":"pith:PNUYYMIK","canonical_record":{"source":{"id":"2410.04045","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-05T05:52:22Z","cross_cats_sorted":[],"title_canon_sha256":"d9b85070cc5edca6622912ba2e3d48fa97661db22751a4431b2d8bec44c6a012","abstract_canon_sha256":"1a3c3a3ef719bed85f554fda9f3ca899eacb2090d502f52a4dbddd6e579eac06"},"schema_version":"1.0"},"canonical_sha256":"7b698c310ae33bd35ade07c6568a64bb55604d55815a2d726207f84859648869","source":{"kind":"arxiv","id":"2410.04045","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.04045","created_at":"2026-07-05T09:16:28Z"},{"alias_kind":"arxiv_version","alias_value":"2410.04045v1","created_at":"2026-07-05T09:16:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.04045","created_at":"2026-07-05T09:16:28Z"},{"alias_kind":"pith_short_12","alias_value":"PNUYYMIK4M55","created_at":"2026-07-05T09:16:28Z"},{"alias_kind":"pith_short_16","alias_value":"PNUYYMIK4M55GWW6","created_at":"2026-07-05T09:16:28Z"},{"alias_kind":"pith_short_8","alias_value":"PNUYYMIK","created_at":"2026-07-05T09:16:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:PNUYYMIK4M55GWW6A7DFNCTEXN","target":"record","payload":{"canonical_record":{"source":{"id":"2410.04045","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-05T05:52:22Z","cross_cats_sorted":[],"title_canon_sha256":"d9b85070cc5edca6622912ba2e3d48fa97661db22751a4431b2d8bec44c6a012","abstract_canon_sha256":"1a3c3a3ef719bed85f554fda9f3ca899eacb2090d502f52a4dbddd6e579eac06"},"schema_version":"1.0"},"canonical_sha256":"7b698c310ae33bd35ade07c6568a64bb55604d55815a2d726207f84859648869","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:16:28.325190Z","signature_b64":"oQR0xbCJEr7yh7cw9VF7qMEUVnE35NUkjDKhgLWGfHoai0wbKyNfL51HnUgF6bHC6rXvDTqMwPHRoRBsRFhpDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b698c310ae33bd35ade07c6568a64bb55604d55815a2d726207f84859648869","last_reissued_at":"2026-07-05T09:16:28.324615Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:16:28.324615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.04045","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:16:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LAaN3JE0bRIeli22v9cJ7faZt2H4bAeWicmAr+1dCsmG1/NGhhNSDt1V4ldTyIBty8On6hBbjnTy/v+3P7PECg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T09:51:43.555403Z"},"content_sha256":"c7c18a79a3092f046dae008fce3bf2bf965b983af8221abcc1c452a5f67f983e","schema_version":"1.0","event_id":"sha256:c7c18a79a3092f046dae008fce3bf2bf965b983af8221abcc1c452a5f67f983e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:PNUYYMIK4M55GWW6A7DFNCTEXN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neuron-Level Sequential Editing for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"An Zhang, Houcheng Jiang, Junfeng Fang, Ruipeng Wang, Tao Liang, Tianyu Zhang, Xiang Wang","submitted_at":"2024-10-05T05:52:22Z","abstract_excerpt":"This work explores sequential model editing in large language models (LLMs), a critical task that involves modifying internal knowledge within LLMs continuously through multi-round editing, each incorporating updates or corrections to adjust the model outputs without the need for costly retraining. Existing model editing methods, especially those that alter model parameters, typically focus on single-round editing and often face significant challenges in sequential model editing-most notably issues of model forgetting and failure. To address these challenges, we introduce a new model editing m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.04045","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/2410.04045/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":"2026-07-05T09:16:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lkj4imhMmH44IVfyOqK4hI1QY0RT7sX/fqbzO2kNusnNmQgRJNxV71LtDeAKf0hR2PL0n5mLHwE+K/h3xR7WBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T09:51:43.555681Z"},"content_sha256":"4be4705a418b9906d9618819090652d958934783e049b9129a70c549c1a2b4df","schema_version":"1.0","event_id":"sha256:4be4705a418b9906d9618819090652d958934783e049b9129a70c549c1a2b4df"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PNUYYMIK4M55GWW6A7DFNCTEXN/bundle.json","state_url":"https://pith.science/pith/PNUYYMIK4M55GWW6A7DFNCTEXN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PNUYYMIK4M55GWW6A7DFNCTEXN/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":"2026-08-01T09:51:43Z","links":{"resolver":"https://pith.science/pith/PNUYYMIK4M55GWW6A7DFNCTEXN","bundle":"https://pith.science/pith/PNUYYMIK4M55GWW6A7DFNCTEXN/bundle.json","state":"https://pith.science/pith/PNUYYMIK4M55GWW6A7DFNCTEXN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PNUYYMIK4M55GWW6A7DFNCTEXN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:PNUYYMIK4M55GWW6A7DFNCTEXN","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":"1a3c3a3ef719bed85f554fda9f3ca899eacb2090d502f52a4dbddd6e579eac06","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-05T05:52:22Z","title_canon_sha256":"d9b85070cc5edca6622912ba2e3d48fa97661db22751a4431b2d8bec44c6a012"},"schema_version":"1.0","source":{"id":"2410.04045","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.04045","created_at":"2026-07-05T09:16:28Z"},{"alias_kind":"arxiv_version","alias_value":"2410.04045v1","created_at":"2026-07-05T09:16:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.04045","created_at":"2026-07-05T09:16:28Z"},{"alias_kind":"pith_short_12","alias_value":"PNUYYMIK4M55","created_at":"2026-07-05T09:16:28Z"},{"alias_kind":"pith_short_16","alias_value":"PNUYYMIK4M55GWW6","created_at":"2026-07-05T09:16:28Z"},{"alias_kind":"pith_short_8","alias_value":"PNUYYMIK","created_at":"2026-07-05T09:16:28Z"}],"graph_snapshots":[{"event_id":"sha256:4be4705a418b9906d9618819090652d958934783e049b9129a70c549c1a2b4df","target":"graph","created_at":"2026-07-05T09:16:28Z","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/2410.04045/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This work explores sequential model editing in large language models (LLMs), a critical task that involves modifying internal knowledge within LLMs continuously through multi-round editing, each incorporating updates or corrections to adjust the model outputs without the need for costly retraining. Existing model editing methods, especially those that alter model parameters, typically focus on single-round editing and often face significant challenges in sequential model editing-most notably issues of model forgetting and failure. To address these challenges, we introduce a new model editing m","authors_text":"An Zhang, Houcheng Jiang, Junfeng Fang, Ruipeng Wang, Tao Liang, Tianyu Zhang, Xiang Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-05T05:52:22Z","title":"Neuron-Level Sequential Editing for Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.04045","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:c7c18a79a3092f046dae008fce3bf2bf965b983af8221abcc1c452a5f67f983e","target":"record","created_at":"2026-07-05T09:16:28Z","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":"1a3c3a3ef719bed85f554fda9f3ca899eacb2090d502f52a4dbddd6e579eac06","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-05T05:52:22Z","title_canon_sha256":"d9b85070cc5edca6622912ba2e3d48fa97661db22751a4431b2d8bec44c6a012"},"schema_version":"1.0","source":{"id":"2410.04045","kind":"arxiv","version":1}},"canonical_sha256":"7b698c310ae33bd35ade07c6568a64bb55604d55815a2d726207f84859648869","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7b698c310ae33bd35ade07c6568a64bb55604d55815a2d726207f84859648869","first_computed_at":"2026-07-05T09:16:28.324615Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:16:28.324615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oQR0xbCJEr7yh7cw9VF7qMEUVnE35NUkjDKhgLWGfHoai0wbKyNfL51HnUgF6bHC6rXvDTqMwPHRoRBsRFhpDg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:16:28.325190Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.04045","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c7c18a79a3092f046dae008fce3bf2bf965b983af8221abcc1c452a5f67f983e","sha256:4be4705a418b9906d9618819090652d958934783e049b9129a70c549c1a2b4df"],"state_sha256":"3798f830a9762ee3c5ce5293db05cbd850aacf879a9c6129bf49b9d665d9d18a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jOjg5DsyAYCAlZLEHd8TDHTdSn/T9UmZthQ6sVlR5P7zl8bka4C9uovek5kyAt/aTZ1vjkFUQtUnwjopYf9DAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T09:51:43.558397Z","bundle_sha256":"223f858a4e297dfd32b7cfbe60579df3f74291e628bc3d5267cedb8ef4049bc7"}}