{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5DLVTOMYETGVIRXFU5NOR25R4U","short_pith_number":"pith:5DLVTOMY","schema_version":"1.0","canonical_sha256":"e8d759b99824cd5446e5a75ae8ebb1e5302254f7aa695a2d17de2376a5442e3c","source":{"kind":"arxiv","id":"2501.02460","version":3},"attestation_state":"computed","paper":{"title":"Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Pingjie Wang, Shuyang Jiang, Yanfeng Wang, Yiqiu Guo, Yusheng Liao, Yu Wang, Zhe Chen","submitted_at":"2025-01-05T07:03:14Z","abstract_excerpt":"Large language models hold promise for addressing medical challenges, such as medical diagnosis reasoning, research knowledge acquisition, clinical decision-making, and consumer health inquiry support. However, they often generate hallucinations due to limited medical knowledge. Incorporating external knowledge is therefore critical, which necessitates multi-source knowledge acquisition. We address this challenge by framing it as a source planning problem, which is to formulate context-appropriate queries tailored to the attributes of diverse sources. Existing approaches either overlook source"},"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":"2501.02460","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-05T07:03:14Z","cross_cats_sorted":[],"title_canon_sha256":"a1a78fff7035136729352713c9de4dab213cf5fab7268c77bd29908c15646418","abstract_canon_sha256":"20b9f250e5c2fd75fad86c4b6056c1118ba7af92cff88f4f29b3dcc62233ae21"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:18.203880Z","signature_b64":"wD6EEdanuHOn8LxKqw8lbTYFPiix532Wi9BUk90KYNmmgjc7bU5M9injmfpakMVZliEke2sJ2fBjn22jBqmFBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8d759b99824cd5446e5a75ae8ebb1e5302254f7aa695a2d17de2376a5442e3c","last_reissued_at":"2026-07-05T11:13:18.203369Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:18.203369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Pingjie Wang, Shuyang Jiang, Yanfeng Wang, Yiqiu Guo, Yusheng Liao, Yu Wang, Zhe Chen","submitted_at":"2025-01-05T07:03:14Z","abstract_excerpt":"Large language models hold promise for addressing medical challenges, such as medical diagnosis reasoning, research knowledge acquisition, clinical decision-making, and consumer health inquiry support. However, they often generate hallucinations due to limited medical knowledge. Incorporating external knowledge is therefore critical, which necessitates multi-source knowledge acquisition. We address this challenge by framing it as a source planning problem, which is to formulate context-appropriate queries tailored to the attributes of diverse sources. Existing approaches either overlook source"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.02460","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/2501.02460/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":"2501.02460","created_at":"2026-07-05T11:13:18.203439+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.02460v3","created_at":"2026-07-05T11:13:18.203439+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.02460","created_at":"2026-07-05T11:13:18.203439+00:00"},{"alias_kind":"pith_short_12","alias_value":"5DLVTOMYETGV","created_at":"2026-07-05T11:13:18.203439+00:00"},{"alias_kind":"pith_short_16","alias_value":"5DLVTOMYETGVIRXF","created_at":"2026-07-05T11:13:18.203439+00:00"},{"alias_kind":"pith_short_8","alias_value":"5DLVTOMY","created_at":"2026-07-05T11:13:18.203439+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.12774","citing_title":"A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models","ref_index":56,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5DLVTOMYETGVIRXFU5NOR25R4U","json":"https://pith.science/pith/5DLVTOMYETGVIRXFU5NOR25R4U.json","graph_json":"https://pith.science/api/pith-number/5DLVTOMYETGVIRXFU5NOR25R4U/graph.json","events_json":"https://pith.science/api/pith-number/5DLVTOMYETGVIRXFU5NOR25R4U/events.json","paper":"https://pith.science/paper/5DLVTOMY"},"agent_actions":{"view_html":"https://pith.science/pith/5DLVTOMYETGVIRXFU5NOR25R4U","download_json":"https://pith.science/pith/5DLVTOMYETGVIRXFU5NOR25R4U.json","view_paper":"https://pith.science/paper/5DLVTOMY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.02460&json=true","fetch_graph":"https://pith.science/api/pith-number/5DLVTOMYETGVIRXFU5NOR25R4U/graph.json","fetch_events":"https://pith.science/api/pith-number/5DLVTOMYETGVIRXFU5NOR25R4U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5DLVTOMYETGVIRXFU5NOR25R4U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5DLVTOMYETGVIRXFU5NOR25R4U/action/storage_attestation","attest_author":"https://pith.science/pith/5DLVTOMYETGVIRXFU5NOR25R4U/action/author_attestation","sign_citation":"https://pith.science/pith/5DLVTOMYETGVIRXFU5NOR25R4U/action/citation_signature","submit_replication":"https://pith.science/pith/5DLVTOMYETGVIRXFU5NOR25R4U/action/replication_record"}},"created_at":"2026-07-05T11:13:18.203439+00:00","updated_at":"2026-07-05T11:13:18.203439+00:00"}