{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:REMCTYK5MDY3LVSX6M4HU3S2JI","short_pith_number":"pith:REMCTYK5","schema_version":"1.0","canonical_sha256":"891829e15d60f1b5d657f3387a6e5a4a2449449a457ffdaf865281e1289c628f","source":{"kind":"arxiv","id":"2310.08475","version":5},"attestation_state":"computed","paper":{"title":"Can We Edit Multimodal Large Language Models?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.MM"],"primary_cat":"cs.CL","authors_text":"Bozhong Tian, Huajun Chen, Ningyu Zhang, Qingbin Liu, Siyuan Cheng, Xi Chen, Yongheng Wang","submitted_at":"2023-10-12T16:32:44Z","abstract_excerpt":"In this paper, we focus on editing Multimodal Large Language Models (MLLMs). Compared to editing single-modal LLMs, multimodal model editing is more challenging, which demands a higher level of scrutiny and careful consideration in the editing process. To facilitate research in this area, we construct a new benchmark, dubbed MMEdit, for editing multimodal LLMs and establishing a suite of innovative metrics for evaluation. We conduct comprehensive experiments involving various model editing baselines and analyze the impact of editing different components for multimodal LLMs. Empirically, we not"},"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":"2310.08475","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-12T16:32:44Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.MM"],"title_canon_sha256":"950988d7a2da2b4928e1929910bb7761ea96035f64fcf4651187f8e15bfa641b","abstract_canon_sha256":"ad75fcdfca3cf5355bf33b799866206611607fee5d60e1690824fc119d716bb3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:09:25.586165Z","signature_b64":"pdH0HcSTCs6Jcl7wcfv6bAg4D3x1ZaEeApDLjj+kOX0DVThDuiuqZ3UzDwWeBGQ/bz8lIhS+1Lc1e8fxGXMTAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"891829e15d60f1b5d657f3387a6e5a4a2449449a457ffdaf865281e1289c628f","last_reissued_at":"2026-07-05T08:09:25.585634Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:09:25.585634Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can We Edit Multimodal Large Language Models?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.MM"],"primary_cat":"cs.CL","authors_text":"Bozhong Tian, Huajun Chen, Ningyu Zhang, Qingbin Liu, Siyuan Cheng, Xi Chen, Yongheng Wang","submitted_at":"2023-10-12T16:32:44Z","abstract_excerpt":"In this paper, we focus on editing Multimodal Large Language Models (MLLMs). Compared to editing single-modal LLMs, multimodal model editing is more challenging, which demands a higher level of scrutiny and careful consideration in the editing process. To facilitate research in this area, we construct a new benchmark, dubbed MMEdit, for editing multimodal LLMs and establishing a suite of innovative metrics for evaluation. We conduct comprehensive experiments involving various model editing baselines and analyze the impact of editing different components for multimodal LLMs. Empirically, we not"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08475","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/2310.08475/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":"2310.08475","created_at":"2026-07-05T08:09:25.585681+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.08475v5","created_at":"2026-07-05T08:09:25.585681+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08475","created_at":"2026-07-05T08:09:25.585681+00:00"},{"alias_kind":"pith_short_12","alias_value":"REMCTYK5MDY3","created_at":"2026-07-05T08:09:25.585681+00:00"},{"alias_kind":"pith_short_16","alias_value":"REMCTYK5MDY3LVSX","created_at":"2026-07-05T08:09:25.585681+00:00"},{"alias_kind":"pith_short_8","alias_value":"REMCTYK5","created_at":"2026-07-05T08:09:25.585681+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.17057","citing_title":"Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2605.23780","citing_title":"Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07965","citing_title":"DSCA: Dynamic Subspace Concept Alignment for Lifelong VLM Editing","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/REMCTYK5MDY3LVSX6M4HU3S2JI","json":"https://pith.science/pith/REMCTYK5MDY3LVSX6M4HU3S2JI.json","graph_json":"https://pith.science/api/pith-number/REMCTYK5MDY3LVSX6M4HU3S2JI/graph.json","events_json":"https://pith.science/api/pith-number/REMCTYK5MDY3LVSX6M4HU3S2JI/events.json","paper":"https://pith.science/paper/REMCTYK5"},"agent_actions":{"view_html":"https://pith.science/pith/REMCTYK5MDY3LVSX6M4HU3S2JI","download_json":"https://pith.science/pith/REMCTYK5MDY3LVSX6M4HU3S2JI.json","view_paper":"https://pith.science/paper/REMCTYK5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.08475&json=true","fetch_graph":"https://pith.science/api/pith-number/REMCTYK5MDY3LVSX6M4HU3S2JI/graph.json","fetch_events":"https://pith.science/api/pith-number/REMCTYK5MDY3LVSX6M4HU3S2JI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/REMCTYK5MDY3LVSX6M4HU3S2JI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/REMCTYK5MDY3LVSX6M4HU3S2JI/action/storage_attestation","attest_author":"https://pith.science/pith/REMCTYK5MDY3LVSX6M4HU3S2JI/action/author_attestation","sign_citation":"https://pith.science/pith/REMCTYK5MDY3LVSX6M4HU3S2JI/action/citation_signature","submit_replication":"https://pith.science/pith/REMCTYK5MDY3LVSX6M4HU3S2JI/action/replication_record"}},"created_at":"2026-07-05T08:09:25.585681+00:00","updated_at":"2026-07-05T08:09:25.585681+00:00"}