{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NLYSNF6NNHQWNPDUVREUWSF5VL","short_pith_number":"pith:NLYSNF6N","schema_version":"1.0","canonical_sha256":"6af12697cd69e166bc74ac494b48bdaae098e48b71ee5ba91a935ffa4761b61f","source":{"kind":"arxiv","id":"2502.10455","version":1},"attestation_state":"computed","paper":{"title":"E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal Out-of-Context Misinformation Detection","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.LG","authors_text":"Guohong Fu, Junjie Wu, Nan Yu, Yumeng Fu","submitted_at":"2025-02-12T04:25:14Z","abstract_excerpt":"Recent studies in Large Vision-Language Models (LVLMs) have demonstrated impressive advancements in multimodal Out-of-Context (OOC) misinformation detection, discerning whether an authentic image is wrongly used in a claim. Despite their success, the textual evidence of authentic images retrieved from the inverse search is directly transmitted to LVLMs, leading to inaccurate or false information in the decision-making phase. To this end, we present E2LVLM, a novel evidence-enhanced large vision-language model by adapting textual evidence in two levels. First, motivated by the fact that textual"},"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":"2502.10455","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-12T04:25:14Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"6907f548c52b5b7a180f8bd82d5f22f59f0521e4aed9b76ae644e8c37aa35e5f","abstract_canon_sha256":"a78778e5481c25ac95952aac6e4a601a83fe6f193ea81f6c8a30d02c3b9363f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:52.891529Z","signature_b64":"vPvWVQU2sKDomSIKh9IdN1RCID27txGrPaIH9fS/06JHJoHpEe6AelcRcht1QC/Ax/qM/nxP58biEVjp3A9SDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6af12697cd69e166bc74ac494b48bdaae098e48b71ee5ba91a935ffa4761b61f","last_reissued_at":"2026-07-05T10:14:52.891007Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:52.891007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal Out-of-Context Misinformation Detection","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.LG","authors_text":"Guohong Fu, Junjie Wu, Nan Yu, Yumeng Fu","submitted_at":"2025-02-12T04:25:14Z","abstract_excerpt":"Recent studies in Large Vision-Language Models (LVLMs) have demonstrated impressive advancements in multimodal Out-of-Context (OOC) misinformation detection, discerning whether an authentic image is wrongly used in a claim. Despite their success, the textual evidence of authentic images retrieved from the inverse search is directly transmitted to LVLMs, leading to inaccurate or false information in the decision-making phase. To this end, we present E2LVLM, a novel evidence-enhanced large vision-language model by adapting textual evidence in two levels. First, motivated by the fact that textual"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.10455","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/2502.10455/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":"2502.10455","created_at":"2026-07-05T10:14:52.891065+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.10455v1","created_at":"2026-07-05T10:14:52.891065+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.10455","created_at":"2026-07-05T10:14:52.891065+00:00"},{"alias_kind":"pith_short_12","alias_value":"NLYSNF6NNHQW","created_at":"2026-07-05T10:14:52.891065+00:00"},{"alias_kind":"pith_short_16","alias_value":"NLYSNF6NNHQWNPDU","created_at":"2026-07-05T10:14:52.891065+00:00"},{"alias_kind":"pith_short_8","alias_value":"NLYSNF6N","created_at":"2026-07-05T10:14:52.891065+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/NLYSNF6NNHQWNPDUVREUWSF5VL","json":"https://pith.science/pith/NLYSNF6NNHQWNPDUVREUWSF5VL.json","graph_json":"https://pith.science/api/pith-number/NLYSNF6NNHQWNPDUVREUWSF5VL/graph.json","events_json":"https://pith.science/api/pith-number/NLYSNF6NNHQWNPDUVREUWSF5VL/events.json","paper":"https://pith.science/paper/NLYSNF6N"},"agent_actions":{"view_html":"https://pith.science/pith/NLYSNF6NNHQWNPDUVREUWSF5VL","download_json":"https://pith.science/pith/NLYSNF6NNHQWNPDUVREUWSF5VL.json","view_paper":"https://pith.science/paper/NLYSNF6N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.10455&json=true","fetch_graph":"https://pith.science/api/pith-number/NLYSNF6NNHQWNPDUVREUWSF5VL/graph.json","fetch_events":"https://pith.science/api/pith-number/NLYSNF6NNHQWNPDUVREUWSF5VL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NLYSNF6NNHQWNPDUVREUWSF5VL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NLYSNF6NNHQWNPDUVREUWSF5VL/action/storage_attestation","attest_author":"https://pith.science/pith/NLYSNF6NNHQWNPDUVREUWSF5VL/action/author_attestation","sign_citation":"https://pith.science/pith/NLYSNF6NNHQWNPDUVREUWSF5VL/action/citation_signature","submit_replication":"https://pith.science/pith/NLYSNF6NNHQWNPDUVREUWSF5VL/action/replication_record"}},"created_at":"2026-07-05T10:14:52.891065+00:00","updated_at":"2026-07-05T10:14:52.891065+00:00"}