{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IJ7BIKIEMIVCEPPYCQO6BPVRPH","short_pith_number":"pith:IJ7BIKIE","schema_version":"1.0","canonical_sha256":"427e142904622a223df8141de0beb179f39606048f0f5d73056846de40cbe05a","source":{"kind":"arxiv","id":"2409.15277","version":1},"attestation_state":"computed","paper":{"title":"A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bingchen Zhao, Cihang Xie, Haoqin Tu, Juncheng Wu, Qiao Jin, Siwei Yang, Yongshuo Zong, Yunfei Xie, Yuyin Zhou","submitted_at":"2024-09-23T17:59:43Z","abstract_excerpt":"Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest model, OpenAI's o1, stands out as the first LLM with an internalized chain-of-thought technique using reinforcement learning strategies. While it has demonstrated surprisingly strong capabilities on various general language tasks, its performance in specialized fields such as medicine remains unknown. To this end, this report provides a comprehensive exploration of o1 on different medical scenarios, examining 3 key a"},"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":"2409.15277","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-09-23T17:59:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"32d41a72cb213b727b3e1678f87147517d1d38cd64e3682773d0667fd768a32f","abstract_canon_sha256":"64818f7ad7c0594f9194242f1d5ae69d6e46d9319cee252b6d1487fae483cd5c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:10:38.723740Z","signature_b64":"KwwwhV5zT2LG45hFhS78nnI0vrf9XQR3ksZU2FK00j1nIbGQrcpIeOKS7AJsZIuE0hfyHAembulAoLGR3uKmAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"427e142904622a223df8141de0beb179f39606048f0f5d73056846de40cbe05a","last_reissued_at":"2026-07-05T09:10:38.723239Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:10:38.723239Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bingchen Zhao, Cihang Xie, Haoqin Tu, Juncheng Wu, Qiao Jin, Siwei Yang, Yongshuo Zong, Yunfei Xie, Yuyin Zhou","submitted_at":"2024-09-23T17:59:43Z","abstract_excerpt":"Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest model, OpenAI's o1, stands out as the first LLM with an internalized chain-of-thought technique using reinforcement learning strategies. While it has demonstrated surprisingly strong capabilities on various general language tasks, its performance in specialized fields such as medicine remains unknown. To this end, this report provides a comprehensive exploration of o1 on different medical scenarios, examining 3 key a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.15277","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/2409.15277/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":"2409.15277","created_at":"2026-07-05T09:10:38.723297+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.15277v1","created_at":"2026-07-05T09:10:38.723297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.15277","created_at":"2026-07-05T09:10:38.723297+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJ7BIKIEMIVC","created_at":"2026-07-05T09:10:38.723297+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJ7BIKIEMIVCEPPY","created_at":"2026-07-05T09:10:38.723297+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJ7BIKIE","created_at":"2026-07-05T09:10:38.723297+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.24902","citing_title":"When Reasoning Hurts: Source-Aware Evaluation of Frontier LLMs for Clinical SOAP Note Generation","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2504.12334","citing_title":"QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2512.19691","citing_title":"Scalable Stewardship of an LLM-Assisted Clinical Benchmark with Physician Oversight","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2412.18925","citing_title":"HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IJ7BIKIEMIVCEPPYCQO6BPVRPH","json":"https://pith.science/pith/IJ7BIKIEMIVCEPPYCQO6BPVRPH.json","graph_json":"https://pith.science/api/pith-number/IJ7BIKIEMIVCEPPYCQO6BPVRPH/graph.json","events_json":"https://pith.science/api/pith-number/IJ7BIKIEMIVCEPPYCQO6BPVRPH/events.json","paper":"https://pith.science/paper/IJ7BIKIE"},"agent_actions":{"view_html":"https://pith.science/pith/IJ7BIKIEMIVCEPPYCQO6BPVRPH","download_json":"https://pith.science/pith/IJ7BIKIEMIVCEPPYCQO6BPVRPH.json","view_paper":"https://pith.science/paper/IJ7BIKIE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.15277&json=true","fetch_graph":"https://pith.science/api/pith-number/IJ7BIKIEMIVCEPPYCQO6BPVRPH/graph.json","fetch_events":"https://pith.science/api/pith-number/IJ7BIKIEMIVCEPPYCQO6BPVRPH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJ7BIKIEMIVCEPPYCQO6BPVRPH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJ7BIKIEMIVCEPPYCQO6BPVRPH/action/storage_attestation","attest_author":"https://pith.science/pith/IJ7BIKIEMIVCEPPYCQO6BPVRPH/action/author_attestation","sign_citation":"https://pith.science/pith/IJ7BIKIEMIVCEPPYCQO6BPVRPH/action/citation_signature","submit_replication":"https://pith.science/pith/IJ7BIKIEMIVCEPPYCQO6BPVRPH/action/replication_record"}},"created_at":"2026-07-05T09:10:38.723297+00:00","updated_at":"2026-07-05T09:10:38.723297+00:00"}