{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:OUNK4VOGHKCOYB7ZSIN43VUYQ4","short_pith_number":"pith:OUNK4VOG","canonical_record":{"source":{"id":"2501.09213","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-16T00:19:19Z","cross_cats_sorted":[],"title_canon_sha256":"3cf83f2e2de5a62d6068b133cc3e5c6b189f5445da14f77e9f3f5a3ee9507130","abstract_canon_sha256":"18facd342d631097301e1e5b6453851afb90900845b26a1516c248be9ac3cbfb"},"schema_version":"1.0"},"canonical_sha256":"751aae55c63a84ec07f9921bcdd6988713c9608252a8b341f3616136e7b52b03","source":{"kind":"arxiv","id":"2501.09213","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.09213","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"arxiv_version","alias_value":"2501.09213v3","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09213","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"pith_short_12","alias_value":"OUNK4VOGHKCO","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"pith_short_16","alias_value":"OUNK4VOGHKCOYB7Z","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"pith_short_8","alias_value":"OUNK4VOG","created_at":"2026-07-05T11:45:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:OUNK4VOGHKCOYB7ZSIN43VUYQ4","target":"record","payload":{"canonical_record":{"source":{"id":"2501.09213","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-16T00:19:19Z","cross_cats_sorted":[],"title_canon_sha256":"3cf83f2e2de5a62d6068b133cc3e5c6b189f5445da14f77e9f3f5a3ee9507130","abstract_canon_sha256":"18facd342d631097301e1e5b6453851afb90900845b26a1516c248be9ac3cbfb"},"schema_version":"1.0"},"canonical_sha256":"751aae55c63a84ec07f9921bcdd6988713c9608252a8b341f3616136e7b52b03","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:30.819171Z","signature_b64":"dOjmLtSPT56iaF2GgdRNAFNdKlYdzhbUYH/xPZ62DlUhV/x+0ATLUIFRsHEjaSjmg+HEmd9Mh2NER0gSs5eMDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"751aae55c63a84ec07f9921bcdd6988713c9608252a8b341f3616136e7b52b03","last_reissued_at":"2026-07-05T11:45:30.818658Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:30.818658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.09213","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:45:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yFuZPlj4sPMalNOaf8AXeueL1QpdgfGiFe+swVWz22C8IcPfEtDn9mH5H3125uggvcwOfUVigUSJHSlm7uPHBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:55:34.308148Z"},"content_sha256":"479d7bd5499420ac4190cd4d6f14906bdde2ea7bf31e8a6c728346c8353fb130","schema_version":"1.0","event_id":"sha256:479d7bd5499420ac4190cd4d6f14906bdde2ea7bf31e8a6c728346c8353fb130"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:OUNK4VOGHKCOYB7ZSIN43VUYQ4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hongzhou Yu, Qing Wang, Rui Feng, Tianhao Cheng, Wen He, Xiaobo Zhang, Ying Cheng, Yingwen Wang, Yuejie Zhang","submitted_at":"2025-01-16T00:19:19Z","abstract_excerpt":"Recent advancements in large language models (LLMs) have shown promise in medical applications such as disease diagnosis and treatment planning. However, most existing medical LLMs struggle with the deep reasoning required for complex medical problems, such as differential diagnosis and medication recommendations. We propose FineMedLM-o1, which leverages high-quality medical synthetic data and long-form reasoning data for Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), enabling advanced dialogue and deep reasoning capabilities. Additionally, we introduce Test-Time Traini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09213","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/2501.09213/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:45:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cd+MgMqMaMZ22fv4fJxMDjxpb3qz8/b6SEzSENf5GqsEm7tFDX+bd++e6IL1QdA41YkM2PLfcFhE0wKM82QiAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:55:34.308938Z"},"content_sha256":"16eb8eb429f09c9a95c1efdc3c6133d0fb1a4b907b6ffecd0c259c881103a991","schema_version":"1.0","event_id":"sha256:16eb8eb429f09c9a95c1efdc3c6133d0fb1a4b907b6ffecd0c259c881103a991"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OUNK4VOGHKCOYB7ZSIN43VUYQ4/bundle.json","state_url":"https://pith.science/pith/OUNK4VOGHKCOYB7ZSIN43VUYQ4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OUNK4VOGHKCOYB7ZSIN43VUYQ4/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-18T21:55:34Z","links":{"resolver":"https://pith.science/pith/OUNK4VOGHKCOYB7ZSIN43VUYQ4","bundle":"https://pith.science/pith/OUNK4VOGHKCOYB7ZSIN43VUYQ4/bundle.json","state":"https://pith.science/pith/OUNK4VOGHKCOYB7ZSIN43VUYQ4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OUNK4VOGHKCOYB7ZSIN43VUYQ4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:OUNK4VOGHKCOYB7ZSIN43VUYQ4","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":"18facd342d631097301e1e5b6453851afb90900845b26a1516c248be9ac3cbfb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-16T00:19:19Z","title_canon_sha256":"3cf83f2e2de5a62d6068b133cc3e5c6b189f5445da14f77e9f3f5a3ee9507130"},"schema_version":"1.0","source":{"id":"2501.09213","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.09213","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"arxiv_version","alias_value":"2501.09213v3","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09213","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"pith_short_12","alias_value":"OUNK4VOGHKCO","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"pith_short_16","alias_value":"OUNK4VOGHKCOYB7Z","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"pith_short_8","alias_value":"OUNK4VOG","created_at":"2026-07-05T11:45:30Z"}],"graph_snapshots":[{"event_id":"sha256:16eb8eb429f09c9a95c1efdc3c6133d0fb1a4b907b6ffecd0c259c881103a991","target":"graph","created_at":"2026-07-05T11:45:30Z","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/2501.09213/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in large language models (LLMs) have shown promise in medical applications such as disease diagnosis and treatment planning. However, most existing medical LLMs struggle with the deep reasoning required for complex medical problems, such as differential diagnosis and medication recommendations. We propose FineMedLM-o1, which leverages high-quality medical synthetic data and long-form reasoning data for Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), enabling advanced dialogue and deep reasoning capabilities. Additionally, we introduce Test-Time Traini","authors_text":"Hongzhou Yu, Qing Wang, Rui Feng, Tianhao Cheng, Wen He, Xiaobo Zhang, Ying Cheng, Yingwen Wang, Yuejie Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-16T00:19:19Z","title":"FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09213","kind":"arxiv","version":3},"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":"sha256:479d7bd5499420ac4190cd4d6f14906bdde2ea7bf31e8a6c728346c8353fb130","target":"record","created_at":"2026-07-05T11:45:30Z","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":"18facd342d631097301e1e5b6453851afb90900845b26a1516c248be9ac3cbfb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-16T00:19:19Z","title_canon_sha256":"3cf83f2e2de5a62d6068b133cc3e5c6b189f5445da14f77e9f3f5a3ee9507130"},"schema_version":"1.0","source":{"id":"2501.09213","kind":"arxiv","version":3}},"canonical_sha256":"751aae55c63a84ec07f9921bcdd6988713c9608252a8b341f3616136e7b52b03","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"751aae55c63a84ec07f9921bcdd6988713c9608252a8b341f3616136e7b52b03","first_computed_at":"2026-07-05T11:45:30.818658Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:45:30.818658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dOjmLtSPT56iaF2GgdRNAFNdKlYdzhbUYH/xPZ62DlUhV/x+0ATLUIFRsHEjaSjmg+HEmd9Mh2NER0gSs5eMDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:45:30.819171Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.09213","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:479d7bd5499420ac4190cd4d6f14906bdde2ea7bf31e8a6c728346c8353fb130","sha256:16eb8eb429f09c9a95c1efdc3c6133d0fb1a4b907b6ffecd0c259c881103a991"],"state_sha256":"0a46645666c3311c30c3c9d351e300f1c6d489b3981e23d5bd5c8c8bf5ad76b6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OwzY68n+0lJHvfOEuVscuQbukwyUAj/KfxAbqtr4QuaANSF1nkwI5OWByMu6lvXOjjV2FDD3PXqUHKs6f99XDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T21:55:34.313995Z","bundle_sha256":"68912b1eb10aea88bb9cd20fdf5b478e50ca3047c6ad3e166b9c2ee53046b7f4"}}