{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:HZCTRQ5NL2D4NDXCSRDIEN2UB7","short_pith_number":"pith:HZCTRQ5N","schema_version":"1.0","canonical_sha256":"3e4538c3ad5e87c68ee294468237540fd06a9e3a941e167177a597828535ecc5","source":{"kind":"arxiv","id":"2602.20207","version":3},"attestation_state":"computed","paper":{"title":"Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anshuman Chhabra, Hongfu Liu, Shrestha Datta","submitted_at":"2026-02-22T22:55:11Z","abstract_excerpt":"Knowledge editing in Large Language Models (LLMs) aims to update the model's prediction for a specific query to a desired target while preserving its behavior on all other inputs. This process typically involves two stages: identifying the layer to edit and performing the parameter update. Intuitively, different queries may localize knowledge at different depths of the model, resulting in different sample-wise editing performance for a fixed editing layer. In this work, we hypothesize the existence of fixed golden layers that can achieve near-optimal editing performance similar to sample-wise "},"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":"2602.20207","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-02-22T22:55:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d4001e136a965ae94d3566ac2f591e0edc9c7b2f43223a1d460000eccc8581e9","abstract_canon_sha256":"e4e2800f336c951002815cb514ee9280b579677c9b298ab2f3533716c86a8911"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:00:35.029499Z","signature_b64":"GI0KMyXpBQc9taeKUSiJYFmgnKCx0sXqIZMZ4qtBTnPBqWNYHiF0HEslRNDcInlDdqobpKrK4La1cIZf6i9aBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e4538c3ad5e87c68ee294468237540fd06a9e3a941e167177a597828535ecc5","last_reissued_at":"2026-05-20T00:00:35.028744Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:00:35.028744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anshuman Chhabra, Hongfu Liu, Shrestha Datta","submitted_at":"2026-02-22T22:55:11Z","abstract_excerpt":"Knowledge editing in Large Language Models (LLMs) aims to update the model's prediction for a specific query to a desired target while preserving its behavior on all other inputs. This process typically involves two stages: identifying the layer to edit and performing the parameter update. Intuitively, different queries may localize knowledge at different depths of the model, resulting in different sample-wise editing performance for a fixed editing layer. In this work, we hypothesize the existence of fixed golden layers that can achieve near-optimal editing performance similar to sample-wise "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.20207","kind":"arxiv","version":3},"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/2602.20207/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":"2602.20207","created_at":"2026-05-20T00:00:35.028843+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.20207v3","created_at":"2026-05-20T00:00:35.028843+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.20207","created_at":"2026-05-20T00:00:35.028843+00:00"},{"alias_kind":"pith_short_12","alias_value":"HZCTRQ5NL2D4","created_at":"2026-05-20T00:00:35.028843+00:00"},{"alias_kind":"pith_short_16","alias_value":"HZCTRQ5NL2D4NDXC","created_at":"2026-05-20T00:00:35.028843+00:00"},{"alias_kind":"pith_short_8","alias_value":"HZCTRQ5N","created_at":"2026-05-20T00:00:35.028843+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HZCTRQ5NL2D4NDXCSRDIEN2UB7","json":"https://pith.science/pith/HZCTRQ5NL2D4NDXCSRDIEN2UB7.json","graph_json":"https://pith.science/api/pith-number/HZCTRQ5NL2D4NDXCSRDIEN2UB7/graph.json","events_json":"https://pith.science/api/pith-number/HZCTRQ5NL2D4NDXCSRDIEN2UB7/events.json","paper":"https://pith.science/paper/HZCTRQ5N"},"agent_actions":{"view_html":"https://pith.science/pith/HZCTRQ5NL2D4NDXCSRDIEN2UB7","download_json":"https://pith.science/pith/HZCTRQ5NL2D4NDXCSRDIEN2UB7.json","view_paper":"https://pith.science/paper/HZCTRQ5N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.20207&json=true","fetch_graph":"https://pith.science/api/pith-number/HZCTRQ5NL2D4NDXCSRDIEN2UB7/graph.json","fetch_events":"https://pith.science/api/pith-number/HZCTRQ5NL2D4NDXCSRDIEN2UB7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HZCTRQ5NL2D4NDXCSRDIEN2UB7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HZCTRQ5NL2D4NDXCSRDIEN2UB7/action/storage_attestation","attest_author":"https://pith.science/pith/HZCTRQ5NL2D4NDXCSRDIEN2UB7/action/author_attestation","sign_citation":"https://pith.science/pith/HZCTRQ5NL2D4NDXCSRDIEN2UB7/action/citation_signature","submit_replication":"https://pith.science/pith/HZCTRQ5NL2D4NDXCSRDIEN2UB7/action/replication_record"}},"created_at":"2026-05-20T00:00:35.028843+00:00","updated_at":"2026-05-20T00:00:35.028843+00:00"}