{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KGQDAYMY2DYRXXF6YO5LZO2D7I","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":"cbfc0b68870662f8a283c85095960fc10f2c0995f7d1a8eb5cf024bc9317d2c6","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-26T17:14:43Z","title_canon_sha256":"5b8606c874d12dc7f8fb3f21f9a81f9217a6b2a3fc7f37c8bfd9a52a096cec3a"},"schema_version":"1.0","source":{"id":"2305.17100","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.17100","created_at":"2026-07-05T08:54:02Z"},{"alias_kind":"arxiv_version","alias_value":"2305.17100v4","created_at":"2026-07-05T08:54:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.17100","created_at":"2026-07-05T08:54:02Z"},{"alias_kind":"pith_short_12","alias_value":"KGQDAYMY2DYR","created_at":"2026-07-05T08:54:02Z"},{"alias_kind":"pith_short_16","alias_value":"KGQDAYMY2DYRXXF6","created_at":"2026-07-05T08:54:02Z"},{"alias_kind":"pith_short_8","alias_value":"KGQDAYMY","created_at":"2026-07-05T08:54:02Z"}],"graph_snapshots":[{"event_id":"sha256:020d8a33e50321e7a2ae610b682af333accedd7c79cafa758e03578d6feee0fd","target":"graph","created_at":"2026-07-05T08:54:02Z","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/2305.17100/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. However, existing biomedical generalist AI solutions are typically heavyweight and closed source to researchers, practitioners, and patients. Here, we propose BiomedGPT, the first open-source and lightweight vision-lan","authors_text":"Brian D. Davison, Chen Chen, Eashan Adhikarla, Hongfang Liu, Hui Ren, James Zou, Jing Huang, Jun Yu, Kai Zhang, Lichao Sun, Lifang He, Quanzheng Li, Rong Zhou, Sunyang Fu, Tianming Liu, Wei Liu, Xiang Li, Xun Chen, Yixin Liu, Yong Chen, Yuyin Zhou, Zhengliang Liu, Zhiling Yan","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-26T17:14:43Z","title":"BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.17100","kind":"arxiv","version":4},"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":"sha256:6653eb9d5c31f4a0a3279a65b7b14bdf4cf73df7cf569e0badcd389813b629c5","target":"record","created_at":"2026-07-05T08:54:02Z","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":"cbfc0b68870662f8a283c85095960fc10f2c0995f7d1a8eb5cf024bc9317d2c6","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-26T17:14:43Z","title_canon_sha256":"5b8606c874d12dc7f8fb3f21f9a81f9217a6b2a3fc7f37c8bfd9a52a096cec3a"},"schema_version":"1.0","source":{"id":"2305.17100","kind":"arxiv","version":4}},"canonical_sha256":"51a0306198d0f11bdcbec3babcbb43fa25d7194a6336756bb8cda3d96bbfb9d1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"51a0306198d0f11bdcbec3babcbb43fa25d7194a6336756bb8cda3d96bbfb9d1","first_computed_at":"2026-07-05T08:54:02.235599Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:54:02.235599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"imRWCOUptfC/VhTOjgXwmDkigsD5HzeCJ0NpO8jD63mcsjodT0Qa42IvinjdJyRTxcsfo11VSQNVTsHokQuvBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:54:02.236061Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.17100","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6653eb9d5c31f4a0a3279a65b7b14bdf4cf73df7cf569e0badcd389813b629c5","sha256:020d8a33e50321e7a2ae610b682af333accedd7c79cafa758e03578d6feee0fd"],"state_sha256":"700dcee89a3853bd2e7243c04a3e369a7399e316bd5abe809437ba07a3ccfefd"}