{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NJGMGVPBIGDM4PQYGFAMAYOPAU","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":"a7e6c3ee9cc9326b45b0c55ad5868bb6b8d0dfb7d0797f5d5071453cff5ddb8e","cross_cats_sorted":["cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-16T23:03:27Z","title_canon_sha256":"7e18ffa77b3bfd91982f3804f0a9512f1aa08e0ab4b414be2f0a21e51b75737b"},"schema_version":"1.0","source":{"id":"2410.13085","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13085","created_at":"2026-07-05T10:22:52Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13085v2","created_at":"2026-07-05T10:22:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13085","created_at":"2026-07-05T10:22:52Z"},{"alias_kind":"pith_short_12","alias_value":"NJGMGVPBIGDM","created_at":"2026-07-05T10:22:52Z"},{"alias_kind":"pith_short_16","alias_value":"NJGMGVPBIGDM4PQY","created_at":"2026-07-05T10:22:52Z"},{"alias_kind":"pith_short_8","alias_value":"NJGMGVPB","created_at":"2026-07-05T10:22:52Z"}],"graph_snapshots":[{"event_id":"sha256:895f84a7b5dbc7743ba195b27fccb1d69d36ad6e88f0523ae2e917f8f9ea538a","target":"graph","created_at":"2026-07-05T10:22:52Z","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/2410.13085/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment planning. Recent progress in Medical Large Vision-Language Models (Med-LVLMs) has opened up new possibilities for interactive diagnostic tools. However, these models often suffer from factual hallucination, which can lead to incorrect diagnoses. Fine-tuning and retrieval-augmented generation (RAG) have emerged as methods to address these issues. However, the amount of high-quality data and distribution shifts between training data and deployment data limit the appl","authors_text":"Haoran Li, Huaxiu Yao, James Zou, Kangyu Zhu, Linjun Zhang, Peng Xia, Sheng Wang, Tianze Wang, Weijia Shi","cross_cats":["cs.CL","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-16T23:03:27Z","title":"MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13085","kind":"arxiv","version":2},"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":"sha256:7d9ef15f97373089000f09f85d421e2a433c82b1c3dc1ab000d548466ae63552","target":"record","created_at":"2026-07-05T10:22:52Z","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":"a7e6c3ee9cc9326b45b0c55ad5868bb6b8d0dfb7d0797f5d5071453cff5ddb8e","cross_cats_sorted":["cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-16T23:03:27Z","title_canon_sha256":"7e18ffa77b3bfd91982f3804f0a9512f1aa08e0ab4b414be2f0a21e51b75737b"},"schema_version":"1.0","source":{"id":"2410.13085","kind":"arxiv","version":2}},"canonical_sha256":"6a4cc355e14186ce3e183140c061cf05111a8232ef09b9e5348686bf1662d9d3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6a4cc355e14186ce3e183140c061cf05111a8232ef09b9e5348686bf1662d9d3","first_computed_at":"2026-07-05T10:22:52.383878Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:22:52.383878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jKhScntLAArU7LrvX2qJV/S6PTqDtfc4I4zQlw/tDwrtIpx4qbhrO29GalhranfpyA++W90gC2e73wvEKC76Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:22:52.384800Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.13085","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7d9ef15f97373089000f09f85d421e2a433c82b1c3dc1ab000d548466ae63552","sha256:895f84a7b5dbc7743ba195b27fccb1d69d36ad6e88f0523ae2e917f8f9ea538a"],"state_sha256":"957b3736da5f3c8ea0680d5a2025aee2c442dff55ccdf679e73e7ba4966b3e6b"}