{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LAW2RYR3RJK3AYIHYTHRXK2RJS","short_pith_number":"pith:LAW2RYR3","schema_version":"1.0","canonical_sha256":"582da8e23b8a55b06107c4cf1bab514c9d2ded3c7c0e424fb44ea9e117640117","source":{"kind":"arxiv","id":"2401.01286","version":5},"attestation_state":"computed","paper":{"title":"A Comprehensive Study of Knowledge Editing for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.HC","cs.LG"],"primary_cat":"cs.CL","authors_text":"Bozhong Tian, Fei Huang, Huajun Chen, Jia-Chen Gu, Jintian Zhang, Jun Zhou, Lei Liang, Mengru Wang, Ningyu Zhang, Pengjun Xie, Peng Wang, Shengyu Mao, Shumin Deng, Siyuan Cheng, Xiaowei Zhu, Xin Xu, Yong Jiang, Yuansheng Ni, Yunzhi Yao, Zekun Xi, Zhiqiang Zhang, Ziwen Xu","submitted_at":"2024-01-02T16:54:58Z","abstract_excerpt":"Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising from their extensive parameterization. This challenge is further intensified by the dynamic nature of the world, necessitating frequent updates to LLMs to correct outdated information or integrate new knowledge, thereby ensuring their continued relevance. Note that many applications demand continual model adjustments post-training to address deficienc"},"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":"2401.01286","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-02T16:54:58Z","cross_cats_sorted":["cs.AI","cs.CV","cs.HC","cs.LG"],"title_canon_sha256":"fb33ead6d724676e3be6275acbf3828d8c33f7f73ef9d874adb446ec99159475","abstract_canon_sha256":"a287861bbb05885e9e6fd80f03e7b745ac8a758ceb4d478084123a3d1e281a19"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:09.181175Z","signature_b64":"LkJOZuNSutlXXw5S2JhFDCnBmQ16R6usVTc7oQO0/O7BLlGL8YzWNoyMOVk/HBrOvN6hLDH573x1nNtK2OBXBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"582da8e23b8a55b06107c4cf1bab514c9d2ded3c7c0e424fb44ea9e117640117","last_reissued_at":"2026-07-05T09:36:09.180669Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:09.180669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comprehensive Study of Knowledge Editing for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.HC","cs.LG"],"primary_cat":"cs.CL","authors_text":"Bozhong Tian, Fei Huang, Huajun Chen, Jia-Chen Gu, Jintian Zhang, Jun Zhou, Lei Liang, Mengru Wang, Ningyu Zhang, Pengjun Xie, Peng Wang, Shengyu Mao, Shumin Deng, Siyuan Cheng, Xiaowei Zhu, Xin Xu, Yong Jiang, Yuansheng Ni, Yunzhi Yao, Zekun Xi, Zhiqiang Zhang, Ziwen Xu","submitted_at":"2024-01-02T16:54:58Z","abstract_excerpt":"Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising from their extensive parameterization. This challenge is further intensified by the dynamic nature of the world, necessitating frequent updates to LLMs to correct outdated information or integrate new knowledge, thereby ensuring their continued relevance. Note that many applications demand continual model adjustments post-training to address deficienc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.01286","kind":"arxiv","version":5},"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/2401.01286/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":"2401.01286","created_at":"2026-07-05T09:36:09.180732+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.01286v5","created_at":"2026-07-05T09:36:09.180732+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.01286","created_at":"2026-07-05T09:36:09.180732+00:00"},{"alias_kind":"pith_short_12","alias_value":"LAW2RYR3RJK3","created_at":"2026-07-05T09:36:09.180732+00:00"},{"alias_kind":"pith_short_16","alias_value":"LAW2RYR3RJK3AYIH","created_at":"2026-07-05T09:36:09.180732+00:00"},{"alias_kind":"pith_short_8","alias_value":"LAW2RYR3","created_at":"2026-07-05T09:36:09.180732+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":31,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23276","citing_title":"Exposing the Illusion of Erasure in Knowledge Editing for LLMs","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2606.19679","citing_title":"LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2607.01978","citing_title":"Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2607.02052","citing_title":"Mitigating Package Hallucinations in Large Language Models via Model Editing","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10554","citing_title":"Benchmarking Knowledge Editing using Logical Rules","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03096","citing_title":"Can Factual Opinions Be Edited (Manipulated) in Large Language Models?","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01610","citing_title":"Revisiting Ripple Effects in Knowledge Editing through Pressure-Aware Joint Neighborhood Optimization","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28303","citing_title":"From Fact Overwriting to Knowledge Evolution: Causal Editing via On-Policy Self-Distillation","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29826","citing_title":"Towards Localized and Disentangled Knowledge Editing for Multimodal Large Language Models","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00570","citing_title":"Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence","ref_index":116,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23780","citing_title":"Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2407.13193","citing_title":"Retrieval-Augmented Generation for Natural Language Processing: A Survey","ref_index":197,"is_internal_anchor":false},{"citing_arxiv_id":"2602.20207","citing_title":"Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20273","citing_title":"Modality-Decoupled Online Recursive Editing","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08143","citing_title":"HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2508.01302","citing_title":"Aligning Language Models with Real-time Knowledge Editing","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2602.15823","citing_title":"CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2603.12677","citing_title":"MetaKE: Meta-Learning for Knowledge Editing Toward a Better Accuracy-Editability Trade-off","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2507.03724","citing_title":"MemOS: A Memory OS for AI System","ref_index":72,"is_internal_anchor":false},{"citing_arxiv_id":"2404.13501","citing_title":"A Survey on the Memory Mechanism of Large Language Model based Agents","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04036","citing_title":"MisEdu-RAG: A Misconception-Aware Dual-Hypergraph RAG for Novice Math Teachers","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2604.27251","citing_title":"Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26686","citing_title":"When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08143","citing_title":"HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10146","citing_title":"Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LAW2RYR3RJK3AYIHYTHRXK2RJS","json":"https://pith.science/pith/LAW2RYR3RJK3AYIHYTHRXK2RJS.json","graph_json":"https://pith.science/api/pith-number/LAW2RYR3RJK3AYIHYTHRXK2RJS/graph.json","events_json":"https://pith.science/api/pith-number/LAW2RYR3RJK3AYIHYTHRXK2RJS/events.json","paper":"https://pith.science/paper/LAW2RYR3"},"agent_actions":{"view_html":"https://pith.science/pith/LAW2RYR3RJK3AYIHYTHRXK2RJS","download_json":"https://pith.science/pith/LAW2RYR3RJK3AYIHYTHRXK2RJS.json","view_paper":"https://pith.science/paper/LAW2RYR3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.01286&json=true","fetch_graph":"https://pith.science/api/pith-number/LAW2RYR3RJK3AYIHYTHRXK2RJS/graph.json","fetch_events":"https://pith.science/api/pith-number/LAW2RYR3RJK3AYIHYTHRXK2RJS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LAW2RYR3RJK3AYIHYTHRXK2RJS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LAW2RYR3RJK3AYIHYTHRXK2RJS/action/storage_attestation","attest_author":"https://pith.science/pith/LAW2RYR3RJK3AYIHYTHRXK2RJS/action/author_attestation","sign_citation":"https://pith.science/pith/LAW2RYR3RJK3AYIHYTHRXK2RJS/action/citation_signature","submit_replication":"https://pith.science/pith/LAW2RYR3RJK3AYIHYTHRXK2RJS/action/replication_record"}},"created_at":"2026-07-05T09:36:09.180732+00:00","updated_at":"2026-07-05T09:36:09.180732+00:00"}