{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ZBX3NHJ3AZLK6UAEEJO5RVPM3C","short_pith_number":"pith:ZBX3NHJ3","canonical_record":{"source":{"id":"2307.15189","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-27T20:36:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a11ac7c7d0359a1e42b09220f216f50da51896d062138ca633dec83de5ff6966","abstract_canon_sha256":"d67b51979b024cf52d32767420ff35a2e91497a76c112fdfd96202d8f902ca6b"},"schema_version":"1.0"},"canonical_sha256":"c86fb69d3b0656af5004225dd8d5ecd8a53116b66db8ca9f09fb350d703d2ad3","source":{"kind":"arxiv","id":"2307.15189","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.15189","created_at":"2026-07-05T06:35:31Z"},{"alias_kind":"arxiv_version","alias_value":"2307.15189v1","created_at":"2026-07-05T06:35:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.15189","created_at":"2026-07-05T06:35:31Z"},{"alias_kind":"pith_short_12","alias_value":"ZBX3NHJ3AZLK","created_at":"2026-07-05T06:35:31Z"},{"alias_kind":"pith_short_16","alias_value":"ZBX3NHJ3AZLK6UAE","created_at":"2026-07-05T06:35:31Z"},{"alias_kind":"pith_short_8","alias_value":"ZBX3NHJ3","created_at":"2026-07-05T06:35:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ZBX3NHJ3AZLK6UAEEJO5RVPM3C","target":"record","payload":{"canonical_record":{"source":{"id":"2307.15189","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-27T20:36:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a11ac7c7d0359a1e42b09220f216f50da51896d062138ca633dec83de5ff6966","abstract_canon_sha256":"d67b51979b024cf52d32767420ff35a2e91497a76c112fdfd96202d8f902ca6b"},"schema_version":"1.0"},"canonical_sha256":"c86fb69d3b0656af5004225dd8d5ecd8a53116b66db8ca9f09fb350d703d2ad3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:35:31.639351Z","signature_b64":"ZUtLjt83v7Lkza00H72+oxacFL2qc/oSVp74U/REKpoaXTbIYHvbTNxSAAAjmA8iBuMMkJ9Fg7orpZ3WdJbiBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c86fb69d3b0656af5004225dd8d5ecd8a53116b66db8ca9f09fb350d703d2ad3","last_reissued_at":"2026-07-05T06:35:31.638889Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:35:31.638889Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.15189","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:35:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HNOlXh5dPqyYue0PZDoMbdjS/+UMH9tYKT09Qey4mZsWjhvDfS41BHsgKANGHHqrxNi4hYCR+P8rOhRqt2lrAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:37:46.893725Z"},"content_sha256":"ba42c9c31a3c45bb56321cf70bea307702ace39567d44514b9ac6ea66f7340c7","schema_version":"1.0","event_id":"sha256:ba42c9c31a3c45bb56321cf70bea307702ace39567d44514b9ac6ea66f7340c7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ZBX3NHJ3AZLK6UAEEJO5RVPM3C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Med-Flamingo: a Multimodal Medical Few-shot Learner","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Cyril Zakka, Eduardo Pontes Reis, Jure Leskovec, Michael Moor, Michihiro Yasunaga, Pranav Rajpurkar, Qian Huang, Shirley Wu, Yash Dalmia","submitted_at":"2023-07-27T20:36:02Z","abstract_excerpt":"Medicine, by its nature, is a multifaceted domain that requires the synthesis of information across various modalities. Medical generative vision-language models (VLMs) make a first step in this direction and promise many exciting clinical applications. However, existing models typically have to be fine-tuned on sizeable down-stream datasets, which poses a significant limitation as in many medical applications data is scarce, necessitating models that are capable of learning from few examples in real-time. Here we propose Med-Flamingo, a multimodal few-shot learner adapted to the medical domai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.15189","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/2307.15189/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":"2026-07-05T06:35:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pviyWBvhpqLFmDCbn2zJNA1UchQ1Mr9rbAyTWgDdrAqSkzuuOXAs7EzsyboyX93xxIHhAskCLo/hOo/J7A8kAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:37:46.894707Z"},"content_sha256":"39aa973241093bc41bed6d7f1383231da8348a9c6f0fecdc2fe1cb6b20d0c2f6","schema_version":"1.0","event_id":"sha256:39aa973241093bc41bed6d7f1383231da8348a9c6f0fecdc2fe1cb6b20d0c2f6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZBX3NHJ3AZLK6UAEEJO5RVPM3C/bundle.json","state_url":"https://pith.science/pith/ZBX3NHJ3AZLK6UAEEJO5RVPM3C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZBX3NHJ3AZLK6UAEEJO5RVPM3C/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":"2026-08-10T05:37:46Z","links":{"resolver":"https://pith.science/pith/ZBX3NHJ3AZLK6UAEEJO5RVPM3C","bundle":"https://pith.science/pith/ZBX3NHJ3AZLK6UAEEJO5RVPM3C/bundle.json","state":"https://pith.science/pith/ZBX3NHJ3AZLK6UAEEJO5RVPM3C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZBX3NHJ3AZLK6UAEEJO5RVPM3C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ZBX3NHJ3AZLK6UAEEJO5RVPM3C","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":"d67b51979b024cf52d32767420ff35a2e91497a76c112fdfd96202d8f902ca6b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-27T20:36:02Z","title_canon_sha256":"a11ac7c7d0359a1e42b09220f216f50da51896d062138ca633dec83de5ff6966"},"schema_version":"1.0","source":{"id":"2307.15189","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.15189","created_at":"2026-07-05T06:35:31Z"},{"alias_kind":"arxiv_version","alias_value":"2307.15189v1","created_at":"2026-07-05T06:35:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.15189","created_at":"2026-07-05T06:35:31Z"},{"alias_kind":"pith_short_12","alias_value":"ZBX3NHJ3AZLK","created_at":"2026-07-05T06:35:31Z"},{"alias_kind":"pith_short_16","alias_value":"ZBX3NHJ3AZLK6UAE","created_at":"2026-07-05T06:35:31Z"},{"alias_kind":"pith_short_8","alias_value":"ZBX3NHJ3","created_at":"2026-07-05T06:35:31Z"}],"graph_snapshots":[{"event_id":"sha256:39aa973241093bc41bed6d7f1383231da8348a9c6f0fecdc2fe1cb6b20d0c2f6","target":"graph","created_at":"2026-07-05T06:35:31Z","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/2307.15189/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medicine, by its nature, is a multifaceted domain that requires the synthesis of information across various modalities. Medical generative vision-language models (VLMs) make a first step in this direction and promise many exciting clinical applications. However, existing models typically have to be fine-tuned on sizeable down-stream datasets, which poses a significant limitation as in many medical applications data is scarce, necessitating models that are capable of learning from few examples in real-time. Here we propose Med-Flamingo, a multimodal few-shot learner adapted to the medical domai","authors_text":"Cyril Zakka, Eduardo Pontes Reis, Jure Leskovec, Michael Moor, Michihiro Yasunaga, Pranav Rajpurkar, Qian Huang, Shirley Wu, Yash Dalmia","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-27T20:36:02Z","title":"Med-Flamingo: a Multimodal Medical Few-shot Learner"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.15189","kind":"arxiv","version":1},"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:ba42c9c31a3c45bb56321cf70bea307702ace39567d44514b9ac6ea66f7340c7","target":"record","created_at":"2026-07-05T06:35:31Z","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":"d67b51979b024cf52d32767420ff35a2e91497a76c112fdfd96202d8f902ca6b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-27T20:36:02Z","title_canon_sha256":"a11ac7c7d0359a1e42b09220f216f50da51896d062138ca633dec83de5ff6966"},"schema_version":"1.0","source":{"id":"2307.15189","kind":"arxiv","version":1}},"canonical_sha256":"c86fb69d3b0656af5004225dd8d5ecd8a53116b66db8ca9f09fb350d703d2ad3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c86fb69d3b0656af5004225dd8d5ecd8a53116b66db8ca9f09fb350d703d2ad3","first_computed_at":"2026-07-05T06:35:31.638889Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:35:31.638889Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZUtLjt83v7Lkza00H72+oxacFL2qc/oSVp74U/REKpoaXTbIYHvbTNxSAAAjmA8iBuMMkJ9Fg7orpZ3WdJbiBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:35:31.639351Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.15189","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ba42c9c31a3c45bb56321cf70bea307702ace39567d44514b9ac6ea66f7340c7","sha256:39aa973241093bc41bed6d7f1383231da8348a9c6f0fecdc2fe1cb6b20d0c2f6"],"state_sha256":"d52a51fecbcfc5dcb3f347e1fd901f995c4df21cc713c88e997cf5c0bf6191a3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yg27DlgnQG61m73qcz0ZCgqigHv6x/nzmrVfbxQQMTdTREQKVEqSYn6C+j1oT6dDm2UmX94+aG17tMvCPfomBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T05:37:46.901869Z","bundle_sha256":"0abb0ef64ffa3ac1848bee7425a182240fd8d0aae5a19b92541d69bd1b93f4c6"}}