{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MWTY4KL6G4BBVTQZ4CJKB7NGST","short_pith_number":"pith:MWTY4KL6","canonical_record":{"source":{"id":"2312.01242","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-02T22:57:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2f3276ebb65e5340659bcc1dd54eba73535fc5e316820c40a302446fc84a6a7c","abstract_canon_sha256":"68f8d8d4520224eba5ba50ab62adfe2b3c64652225fbd9984402a36451d9ed85"},"schema_version":"1.0"},"canonical_sha256":"65a78e297e37021ace19e092a0fda694d225e6681b3c341577e217a9af499748","source":{"kind":"arxiv","id":"2312.01242","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.01242","created_at":"2026-07-05T07:19:31Z"},{"alias_kind":"arxiv_version","alias_value":"2312.01242v1","created_at":"2026-07-05T07:19:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.01242","created_at":"2026-07-05T07:19:31Z"},{"alias_kind":"pith_short_12","alias_value":"MWTY4KL6G4BB","created_at":"2026-07-05T07:19:31Z"},{"alias_kind":"pith_short_16","alias_value":"MWTY4KL6G4BBVTQZ","created_at":"2026-07-05T07:19:31Z"},{"alias_kind":"pith_short_8","alias_value":"MWTY4KL6","created_at":"2026-07-05T07:19:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MWTY4KL6G4BBVTQZ4CJKB7NGST","target":"record","payload":{"canonical_record":{"source":{"id":"2312.01242","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-02T22:57:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2f3276ebb65e5340659bcc1dd54eba73535fc5e316820c40a302446fc84a6a7c","abstract_canon_sha256":"68f8d8d4520224eba5ba50ab62adfe2b3c64652225fbd9984402a36451d9ed85"},"schema_version":"1.0"},"canonical_sha256":"65a78e297e37021ace19e092a0fda694d225e6681b3c341577e217a9af499748","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:19:31.671535Z","signature_b64":"qdmOMTtFhH+jRun8R1TYwo5Khh6qMTDYOK87O4e4BRBbWBmfQWSftokQOqi2nJdtUx6Nhhadyi5zGstOPy7JAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"65a78e297e37021ace19e092a0fda694d225e6681b3c341577e217a9af499748","last_reissued_at":"2026-07-05T07:19:31.671153Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:19:31.671153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.01242","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":"2026-07-05T07:19:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DqFqCGmou0yDEdcPKFRFAZqCu9CEb0FuL4aX8iHRceV57lwCfRV+qRIXEZg6i854ecceaWnxsaczZYUNvYTtDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:28:17.505105Z"},"content_sha256":"d64804a196e4e5884ff029975084ba34a65044326e252620c20a5eb5577f448e","schema_version":"1.0","event_id":"sha256:d64804a196e4e5884ff029975084ba34a65044326e252620c20a5eb5577f448e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MWTY4KL6G4BBVTQZ4CJKB7NGST","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DDxT: Deep Generative Transformer Models for Differential Diagnosis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Cynthia Matuszek, Edward Raff, Mohammad Mahmudul Alam, Tim Oates","submitted_at":"2023-12-02T22:57:25Z","abstract_excerpt":"Differential Diagnosis (DDx) is the process of identifying the most likely medical condition among the possible pathologies through the process of elimination based on evidence. An automated process that narrows a large set of pathologies down to the most likely pathologies will be of great importance. The primary prior works have relied on the Reinforcement Learning (RL) paradigm under the intuition that it aligns better with how physicians perform DDx. In this paper, we show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.01242","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/2312.01242/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":"2026-07-05T07:19:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6YZYAK4l+YbRTlyHnutFdySbmCpxZUCYWtplKW7iQJm2t8aWcIXP7TY6i35hFm8Bpgdk7Y/ja0yA2y44OTxjCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:28:17.505499Z"},"content_sha256":"d69dd9429e2af0bbb852f22194921e5ad5c743b7107882dc8ab15b6a17991b48","schema_version":"1.0","event_id":"sha256:d69dd9429e2af0bbb852f22194921e5ad5c743b7107882dc8ab15b6a17991b48"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MWTY4KL6G4BBVTQZ4CJKB7NGST/bundle.json","state_url":"https://pith.science/pith/MWTY4KL6G4BBVTQZ4CJKB7NGST/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MWTY4KL6G4BBVTQZ4CJKB7NGST/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":"2026-07-07T05:28:17Z","links":{"resolver":"https://pith.science/pith/MWTY4KL6G4BBVTQZ4CJKB7NGST","bundle":"https://pith.science/pith/MWTY4KL6G4BBVTQZ4CJKB7NGST/bundle.json","state":"https://pith.science/pith/MWTY4KL6G4BBVTQZ4CJKB7NGST/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MWTY4KL6G4BBVTQZ4CJKB7NGST/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MWTY4KL6G4BBVTQZ4CJKB7NGST","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":"68f8d8d4520224eba5ba50ab62adfe2b3c64652225fbd9984402a36451d9ed85","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-02T22:57:25Z","title_canon_sha256":"2f3276ebb65e5340659bcc1dd54eba73535fc5e316820c40a302446fc84a6a7c"},"schema_version":"1.0","source":{"id":"2312.01242","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.01242","created_at":"2026-07-05T07:19:31Z"},{"alias_kind":"arxiv_version","alias_value":"2312.01242v1","created_at":"2026-07-05T07:19:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.01242","created_at":"2026-07-05T07:19:31Z"},{"alias_kind":"pith_short_12","alias_value":"MWTY4KL6G4BB","created_at":"2026-07-05T07:19:31Z"},{"alias_kind":"pith_short_16","alias_value":"MWTY4KL6G4BBVTQZ","created_at":"2026-07-05T07:19:31Z"},{"alias_kind":"pith_short_8","alias_value":"MWTY4KL6","created_at":"2026-07-05T07:19:31Z"}],"graph_snapshots":[{"event_id":"sha256:d69dd9429e2af0bbb852f22194921e5ad5c743b7107882dc8ab15b6a17991b48","target":"graph","created_at":"2026-07-05T07:19:31Z","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/2312.01242/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Differential Diagnosis (DDx) is the process of identifying the most likely medical condition among the possible pathologies through the process of elimination based on evidence. An automated process that narrows a large set of pathologies down to the most likely pathologies will be of great importance. The primary prior works have relied on the Reinforcement Learning (RL) paradigm under the intuition that it aligns better with how physicians perform DDx. In this paper, we show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior r","authors_text":"Cynthia Matuszek, Edward Raff, Mohammad Mahmudul Alam, Tim Oates","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-02T22:57:25Z","title":"DDxT: Deep Generative Transformer Models for Differential Diagnosis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.01242","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:d64804a196e4e5884ff029975084ba34a65044326e252620c20a5eb5577f448e","target":"record","created_at":"2026-07-05T07:19:31Z","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":"68f8d8d4520224eba5ba50ab62adfe2b3c64652225fbd9984402a36451d9ed85","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-02T22:57:25Z","title_canon_sha256":"2f3276ebb65e5340659bcc1dd54eba73535fc5e316820c40a302446fc84a6a7c"},"schema_version":"1.0","source":{"id":"2312.01242","kind":"arxiv","version":1}},"canonical_sha256":"65a78e297e37021ace19e092a0fda694d225e6681b3c341577e217a9af499748","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"65a78e297e37021ace19e092a0fda694d225e6681b3c341577e217a9af499748","first_computed_at":"2026-07-05T07:19:31.671153Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:19:31.671153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qdmOMTtFhH+jRun8R1TYwo5Khh6qMTDYOK87O4e4BRBbWBmfQWSftokQOqi2nJdtUx6Nhhadyi5zGstOPy7JAA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:19:31.671535Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.01242","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d64804a196e4e5884ff029975084ba34a65044326e252620c20a5eb5577f448e","sha256:d69dd9429e2af0bbb852f22194921e5ad5c743b7107882dc8ab15b6a17991b48"],"state_sha256":"3a74234acd77a4661b06299762e14a707f5b91422109856a55cfaf1858e76328"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZltH4TnrAwKp3HAU9Oz7HwTnv7kHfBS1P4ZaUZOoqFAW22J0RYOmAzDDY762MjZfU7t5Io/h87U7u3B01LngDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T05:28:17.507896Z","bundle_sha256":"3929ef0b48884a793a47ec14bc753427af05104e7d412ae3d205f0506d16c3c7"}}