{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FQM3AFQ6P4R74KLTME5RUUXBIQ","short_pith_number":"pith:FQM3AFQ6","schema_version":"1.0","canonical_sha256":"2c19b0161e7f23fe2973613b1a52e14429228fef647dde2f5ded0c6165c53163","source":{"kind":"arxiv","id":"2504.00993","version":2},"attestation_state":"computed","paper":{"title":"MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chang-In Choi, Hui Ren, Hyunjin Cho, Juncheng Wu, Sheng Liu, Taomian Mi, Wenlong Deng, Xiang Li, Xiaoxiao Li, Xingxuan Li, Yifan Peng, Yihan Cao, Yi Liu, Yuyin Zhou, Ziyang Xu","submitted_at":"2025-04-01T17:31:44Z","abstract_excerpt":"Medical tasks such as diagnosis and treatment planning require precise and complex reasoning, particularly in life-critical domains. Unlike mathematical reasoning, medical reasoning demands meticulous, verifiable thought processes to ensure reliability and accuracy. However, there is a notable lack of datasets that provide transparent, step-by-step reasoning to validate and enhance the medical reasoning ability of AI models. To bridge this gap, we introduce MedReason, a large-scale high-quality medical reasoning dataset designed to enable faithful and explainable medical problem-solving in lar"},"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":"2504.00993","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-01T17:31:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8e17d11f6ebef538d4f63d13d107f133d0fbad00b50f40cf545a0a6c3ee23bbc","abstract_canon_sha256":"d5c10d20a022aa96006d9c9d754c87eef9403fced871dccfb3125dffa3015197"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:44:45.059438Z","signature_b64":"cmtrkI6ol+oqnEpFmUMnHBXlnkGdHfzZRkxgdt6AYNPBOJj+ND9pHB3O/ieAk817JfeAX+6YF3B2lRqJAUq2Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c19b0161e7f23fe2973613b1a52e14429228fef647dde2f5ded0c6165c53163","last_reissued_at":"2026-07-05T10:44:45.058951Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:44:45.058951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chang-In Choi, Hui Ren, Hyunjin Cho, Juncheng Wu, Sheng Liu, Taomian Mi, Wenlong Deng, Xiang Li, Xiaoxiao Li, Xingxuan Li, Yifan Peng, Yihan Cao, Yi Liu, Yuyin Zhou, Ziyang Xu","submitted_at":"2025-04-01T17:31:44Z","abstract_excerpt":"Medical tasks such as diagnosis and treatment planning require precise and complex reasoning, particularly in life-critical domains. Unlike mathematical reasoning, medical reasoning demands meticulous, verifiable thought processes to ensure reliability and accuracy. However, there is a notable lack of datasets that provide transparent, step-by-step reasoning to validate and enhance the medical reasoning ability of AI models. To bridge this gap, we introduce MedReason, a large-scale high-quality medical reasoning dataset designed to enable faithful and explainable medical problem-solving in lar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.00993","kind":"arxiv","version":2},"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/2504.00993/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":"2504.00993","created_at":"2026-07-05T10:44:45.059010+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.00993v2","created_at":"2026-07-05T10:44:45.059010+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.00993","created_at":"2026-07-05T10:44:45.059010+00:00"},{"alias_kind":"pith_short_12","alias_value":"FQM3AFQ6P4R7","created_at":"2026-07-05T10:44:45.059010+00:00"},{"alias_kind":"pith_short_16","alias_value":"FQM3AFQ6P4R74KLT","created_at":"2026-07-05T10:44:45.059010+00:00"},{"alias_kind":"pith_short_8","alias_value":"FQM3AFQ6","created_at":"2026-07-05T10:44:45.059010+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":24,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07761","citing_title":"Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning","ref_index":101,"is_internal_anchor":true},{"citing_arxiv_id":"2604.26283","citing_title":"MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution","ref_index":119,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12569","citing_title":"eCREAM-MedCorpus A Large-Scale Corpus of Clinical Notes for Italian","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12569","citing_title":"eCREAM-MedCorpus A Large-Scale Corpus of Clinical Notes for Italian","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01145","citing_title":"Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches","ref_index":299,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26283","citing_title":"MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution","ref_index":119,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06177","citing_title":"BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30077","citing_title":"Online Data Selection for Instruction Tuning via Gaussian Processes","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00440","citing_title":"SDR: Set-Distance Rewards for Radiology Report Generation","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26283","citing_title":"MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18570","citing_title":"Query-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20176","citing_title":"ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2509.23330","citing_title":"Structured In-context Environment Scaling for Large Language Model Reasoning","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08559","citing_title":"Medical Reasoning with Large Language Models: A Survey and MR-Bench","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2603.27820","citing_title":"Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09505","citing_title":"EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26283","citing_title":"MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09505","citing_title":"EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09584","citing_title":"CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10761","citing_title":"RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology","ref_index":109,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23356","citing_title":"VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs","ref_index":64,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06177","citing_title":"BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01474","citing_title":"ReMedi: Reasoner for Medical Clinical Prediction","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15456","citing_title":"DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FQM3AFQ6P4R74KLTME5RUUXBIQ","json":"https://pith.science/pith/FQM3AFQ6P4R74KLTME5RUUXBIQ.json","graph_json":"https://pith.science/api/pith-number/FQM3AFQ6P4R74KLTME5RUUXBIQ/graph.json","events_json":"https://pith.science/api/pith-number/FQM3AFQ6P4R74KLTME5RUUXBIQ/events.json","paper":"https://pith.science/paper/FQM3AFQ6"},"agent_actions":{"view_html":"https://pith.science/pith/FQM3AFQ6P4R74KLTME5RUUXBIQ","download_json":"https://pith.science/pith/FQM3AFQ6P4R74KLTME5RUUXBIQ.json","view_paper":"https://pith.science/paper/FQM3AFQ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.00993&json=true","fetch_graph":"https://pith.science/api/pith-number/FQM3AFQ6P4R74KLTME5RUUXBIQ/graph.json","fetch_events":"https://pith.science/api/pith-number/FQM3AFQ6P4R74KLTME5RUUXBIQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FQM3AFQ6P4R74KLTME5RUUXBIQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FQM3AFQ6P4R74KLTME5RUUXBIQ/action/storage_attestation","attest_author":"https://pith.science/pith/FQM3AFQ6P4R74KLTME5RUUXBIQ/action/author_attestation","sign_citation":"https://pith.science/pith/FQM3AFQ6P4R74KLTME5RUUXBIQ/action/citation_signature","submit_replication":"https://pith.science/pith/FQM3AFQ6P4R74KLTME5RUUXBIQ/action/replication_record"}},"created_at":"2026-07-05T10:44:45.059010+00:00","updated_at":"2026-07-05T10:44:45.059010+00:00"}