{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:XWHEGG2UUWTBKV6NTZMIYXUEAD","short_pith_number":"pith:XWHEGG2U","canonical_record":{"source":{"id":"2405.00664","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-05-01T17:50:37Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8b1da8d1643c70a7d0723deb26b797890d8e7fbd4abd3063ea972b3525f2de72","abstract_canon_sha256":"8cfe16ae5a02cf4daf938c1c582b693636cd909a08659c27cfdb895d65f430b1"},"schema_version":"1.0"},"canonical_sha256":"bd8e431b54a5a61557cd9e588c5e8400e3f14dfc3c05e2bbe9426e12c9000d1c","source":{"kind":"arxiv","id":"2405.00664","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.00664","created_at":"2026-07-05T08:14:16Z"},{"alias_kind":"arxiv_version","alias_value":"2405.00664v1","created_at":"2026-07-05T08:14:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.00664","created_at":"2026-07-05T08:14:16Z"},{"alias_kind":"pith_short_12","alias_value":"XWHEGG2UUWTB","created_at":"2026-07-05T08:14:16Z"},{"alias_kind":"pith_short_16","alias_value":"XWHEGG2UUWTBKV6N","created_at":"2026-07-05T08:14:16Z"},{"alias_kind":"pith_short_8","alias_value":"XWHEGG2U","created_at":"2026-07-05T08:14:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:XWHEGG2UUWTBKV6NTZMIYXUEAD","target":"record","payload":{"canonical_record":{"source":{"id":"2405.00664","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-05-01T17:50:37Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8b1da8d1643c70a7d0723deb26b797890d8e7fbd4abd3063ea972b3525f2de72","abstract_canon_sha256":"8cfe16ae5a02cf4daf938c1c582b693636cd909a08659c27cfdb895d65f430b1"},"schema_version":"1.0"},"canonical_sha256":"bd8e431b54a5a61557cd9e588c5e8400e3f14dfc3c05e2bbe9426e12c9000d1c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:16.082757Z","signature_b64":"4oOzsvU59d+A2+dOdF9aimsqXTry9yFeVJwhqRX6amu/NvYaRkxx1XMlEBJqD8X6++PEI3YK2Ch2T6nV1S0kAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd8e431b54a5a61557cd9e588c5e8400e3f14dfc3c05e2bbe9426e12c9000d1c","last_reissued_at":"2026-07-05T08:14:16.082323Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:16.082323Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.00664","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":"2026-07-05T08:14:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a6vCZhDHzlbJoaEKPRIxcTllanlDNB3BfqyBXF36lFGSvY6z/JnbSFB+rZRfFpJYnYub/CutqpItfXqXbCR8Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:22:34.299607Z"},"content_sha256":"19fd1b667da7541cd6d76d0436c66a91476179b3bd507912363d471e96bd855f","schema_version":"1.0","event_id":"sha256:19fd1b667da7541cd6d76d0436c66a91476179b3bd507912363d471e96bd855f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:XWHEGG2UUWTBKV6NTZMIYXUEAD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Akshat Gupta, Gopala Anumanchipalli, Junsang Yoon","submitted_at":"2024-05-01T17:50:37Z","abstract_excerpt":"This study presents a targeted model editing analysis focused on the latest large language model, Llama-3. We explore the efficacy of popular model editing techniques - ROME, MEMIT, and EMMET, which are designed for precise layer interventions. We identify the most effective layers for targeted edits through an evaluation that encompasses up to 4096 edits across three distinct strategies: sequential editing, batch editing, and a hybrid approach we call as sequential-batch editing. Our findings indicate that increasing edit batch-sizes may degrade model performance more significantly than using"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.00664","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/2405.00664/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":"2026-07-05T08:14:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pgPYVWywfwMWnI2d7r9XOFQlTBZVOB6+3+qwPpzhbKzGkHuryNs1cNryrGO4WgLjlZSgFZBF74dKs9+FSl7CCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:22:34.300204Z"},"content_sha256":"9029a3baab95a430457db4e3b5c6ebc6848b748883a8402574a4c3cd9a8a84b3","schema_version":"1.0","event_id":"sha256:9029a3baab95a430457db4e3b5c6ebc6848b748883a8402574a4c3cd9a8a84b3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XWHEGG2UUWTBKV6NTZMIYXUEAD/bundle.json","state_url":"https://pith.science/pith/XWHEGG2UUWTBKV6NTZMIYXUEAD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XWHEGG2UUWTBKV6NTZMIYXUEAD/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":"2026-08-09T21:22:34Z","links":{"resolver":"https://pith.science/pith/XWHEGG2UUWTBKV6NTZMIYXUEAD","bundle":"https://pith.science/pith/XWHEGG2UUWTBKV6NTZMIYXUEAD/bundle.json","state":"https://pith.science/pith/XWHEGG2UUWTBKV6NTZMIYXUEAD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XWHEGG2UUWTBKV6NTZMIYXUEAD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:XWHEGG2UUWTBKV6NTZMIYXUEAD","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":"8cfe16ae5a02cf4daf938c1c582b693636cd909a08659c27cfdb895d65f430b1","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-05-01T17:50:37Z","title_canon_sha256":"8b1da8d1643c70a7d0723deb26b797890d8e7fbd4abd3063ea972b3525f2de72"},"schema_version":"1.0","source":{"id":"2405.00664","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.00664","created_at":"2026-07-05T08:14:16Z"},{"alias_kind":"arxiv_version","alias_value":"2405.00664v1","created_at":"2026-07-05T08:14:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.00664","created_at":"2026-07-05T08:14:16Z"},{"alias_kind":"pith_short_12","alias_value":"XWHEGG2UUWTB","created_at":"2026-07-05T08:14:16Z"},{"alias_kind":"pith_short_16","alias_value":"XWHEGG2UUWTBKV6N","created_at":"2026-07-05T08:14:16Z"},{"alias_kind":"pith_short_8","alias_value":"XWHEGG2U","created_at":"2026-07-05T08:14:16Z"}],"graph_snapshots":[{"event_id":"sha256:9029a3baab95a430457db4e3b5c6ebc6848b748883a8402574a4c3cd9a8a84b3","target":"graph","created_at":"2026-07-05T08:14:16Z","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/2405.00664/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study presents a targeted model editing analysis focused on the latest large language model, Llama-3. We explore the efficacy of popular model editing techniques - ROME, MEMIT, and EMMET, which are designed for precise layer interventions. We identify the most effective layers for targeted edits through an evaluation that encompasses up to 4096 edits across three distinct strategies: sequential editing, batch editing, and a hybrid approach we call as sequential-batch editing. Our findings indicate that increasing edit batch-sizes may degrade model performance more significantly than using","authors_text":"Akshat Gupta, Gopala Anumanchipalli, Junsang Yoon","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-05-01T17:50:37Z","title":"Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.00664","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":"sha256:19fd1b667da7541cd6d76d0436c66a91476179b3bd507912363d471e96bd855f","target":"record","created_at":"2026-07-05T08:14:16Z","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":"8cfe16ae5a02cf4daf938c1c582b693636cd909a08659c27cfdb895d65f430b1","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-05-01T17:50:37Z","title_canon_sha256":"8b1da8d1643c70a7d0723deb26b797890d8e7fbd4abd3063ea972b3525f2de72"},"schema_version":"1.0","source":{"id":"2405.00664","kind":"arxiv","version":1}},"canonical_sha256":"bd8e431b54a5a61557cd9e588c5e8400e3f14dfc3c05e2bbe9426e12c9000d1c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bd8e431b54a5a61557cd9e588c5e8400e3f14dfc3c05e2bbe9426e12c9000d1c","first_computed_at":"2026-07-05T08:14:16.082323Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:14:16.082323Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4oOzsvU59d+A2+dOdF9aimsqXTry9yFeVJwhqRX6amu/NvYaRkxx1XMlEBJqD8X6++PEI3YK2Ch2T6nV1S0kAg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:14:16.082757Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.00664","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:19fd1b667da7541cd6d76d0436c66a91476179b3bd507912363d471e96bd855f","sha256:9029a3baab95a430457db4e3b5c6ebc6848b748883a8402574a4c3cd9a8a84b3"],"state_sha256":"d5702eec4737e00af756da2694276a4d56b510bc622e94597cb63c53f6cc0404"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rSpw/K8J7X/ZqVNMspD+4t4c3GXqnpUGZ2MWzoEQ7MPrGue+h3T0hc0fWbRYbsCc9Us3ED7eQ/NodfcgPaC4BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T21:22:34.305359Z","bundle_sha256":"82fefdd175aa4c78ed528f2964562376f8130379b3ad185ea04300c698c58e23"}}