{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ZF6EQNV2AFCWA5ZQNKGSNSRPZJ","short_pith_number":"pith:ZF6EQNV2","canonical_record":{"source":{"id":"2407.02604","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-07-02T18:43:10Z","cross_cats_sorted":["cs.CL","cs.LG","eess.IV"],"title_canon_sha256":"0bb9a083edf363b99e0d49a5c89f9517e619b0dabf9d8edc800e63cfe68478e0","abstract_canon_sha256":"5a8260d8b7f4cabfbc4cb0b36cd2baf4d6c7cfc480c2cf07ae974d346d2725c9"},"schema_version":"1.0"},"canonical_sha256":"c97c4836ba01456077306a8d26ca2fca4e4b4b682dde5f0ce2670686002301ca","source":{"kind":"arxiv","id":"2407.02604","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.02604","created_at":"2026-07-05T08:51:18Z"},{"alias_kind":"arxiv_version","alias_value":"2407.02604v2","created_at":"2026-07-05T08:51:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02604","created_at":"2026-07-05T08:51:18Z"},{"alias_kind":"pith_short_12","alias_value":"ZF6EQNV2AFCW","created_at":"2026-07-05T08:51:18Z"},{"alias_kind":"pith_short_16","alias_value":"ZF6EQNV2AFCWA5ZQ","created_at":"2026-07-05T08:51:18Z"},{"alias_kind":"pith_short_8","alias_value":"ZF6EQNV2","created_at":"2026-07-05T08:51:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ZF6EQNV2AFCWA5ZQNKGSNSRPZJ","target":"record","payload":{"canonical_record":{"source":{"id":"2407.02604","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-07-02T18:43:10Z","cross_cats_sorted":["cs.CL","cs.LG","eess.IV"],"title_canon_sha256":"0bb9a083edf363b99e0d49a5c89f9517e619b0dabf9d8edc800e63cfe68478e0","abstract_canon_sha256":"5a8260d8b7f4cabfbc4cb0b36cd2baf4d6c7cfc480c2cf07ae974d346d2725c9"},"schema_version":"1.0"},"canonical_sha256":"c97c4836ba01456077306a8d26ca2fca4e4b4b682dde5f0ce2670686002301ca","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:51:18.595642Z","signature_b64":"d2mPvehqWbYEdInGCgouXzMv1JdFK++WRbXrcJe/MlT2P+/Z+IxVrtCsNpYXGQI/+sHSV6nOmz9fi1OKnZjgCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c97c4836ba01456077306a8d26ca2fca4e4b4b682dde5f0ce2670686002301ca","last_reissued_at":"2026-07-05T08:51:18.595256Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:51:18.595256Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.02604","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:51:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N1VjrK10lDi/PN1hRf3wkWbFNqYmwus0yY7t2XoK84c8hI+HAgJojQlejs9F0p6ooLAlwTSKnb4vM11H/gnBAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T09:12:32.827684Z"},"content_sha256":"0210c654f117ce4890ed89d452e07e3a3824d111c14db9eacd03c04e3e4c9184","schema_version":"1.0","event_id":"sha256:0210c654f117ce4890ed89d452e07e3a3824d111c14db9eacd03c04e3e4c9184"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ZF6EQNV2AFCWA5ZQNKGSNSRPZJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG","eess.IV"],"primary_cat":"cs.AI","authors_text":"Abhijeet Parida, Hareem Nisar, Holger R. Roth, Marius George Linguraru, Ramon Sanchez-Jacob, Syed Muhammad Anwar, Vishwesh Nath, Zhifan Jiang","submitted_at":"2024-07-02T18:43:10Z","abstract_excerpt":"Large vision language models (VLMs) have progressed incredibly from research to applicability for general-purpose use cases. LLaVA-Med, a pioneering large language and vision assistant for biomedicine, can perform multi-modal biomedical image and data analysis to provide a natural language interface for radiologists. While it is highly generalizable and works with multi-modal data, it is currently limited by well-known challenges that exist in the large language model space. Hallucinations and imprecision in responses can lead to misdiagnosis which currently hinder the clinical adaptability of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02604","kind":"arxiv","version":2},"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/2407.02604/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":"2026-07-05T08:51:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/OJXfmt2gg0Bkx0GECs6VPbYa9w+aUaITdVyOL01WhUYopwFK6/cwnc+e6aZl+WKnazaHboLSG5VfVE4mRB5Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T09:12:32.828059Z"},"content_sha256":"34df62414dfaecc703fe0326c45ba7208ea831cc60d5616c8216a62129cd1ea6","schema_version":"1.0","event_id":"sha256:34df62414dfaecc703fe0326c45ba7208ea831cc60d5616c8216a62129cd1ea6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZF6EQNV2AFCWA5ZQNKGSNSRPZJ/bundle.json","state_url":"https://pith.science/pith/ZF6EQNV2AFCWA5ZQNKGSNSRPZJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZF6EQNV2AFCWA5ZQNKGSNSRPZJ/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":"2026-07-06T09:12:32Z","links":{"resolver":"https://pith.science/pith/ZF6EQNV2AFCWA5ZQNKGSNSRPZJ","bundle":"https://pith.science/pith/ZF6EQNV2AFCWA5ZQNKGSNSRPZJ/bundle.json","state":"https://pith.science/pith/ZF6EQNV2AFCWA5ZQNKGSNSRPZJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZF6EQNV2AFCWA5ZQNKGSNSRPZJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZF6EQNV2AFCWA5ZQNKGSNSRPZJ","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":"5a8260d8b7f4cabfbc4cb0b36cd2baf4d6c7cfc480c2cf07ae974d346d2725c9","cross_cats_sorted":["cs.CL","cs.LG","eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-07-02T18:43:10Z","title_canon_sha256":"0bb9a083edf363b99e0d49a5c89f9517e619b0dabf9d8edc800e63cfe68478e0"},"schema_version":"1.0","source":{"id":"2407.02604","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.02604","created_at":"2026-07-05T08:51:18Z"},{"alias_kind":"arxiv_version","alias_value":"2407.02604v2","created_at":"2026-07-05T08:51:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02604","created_at":"2026-07-05T08:51:18Z"},{"alias_kind":"pith_short_12","alias_value":"ZF6EQNV2AFCW","created_at":"2026-07-05T08:51:18Z"},{"alias_kind":"pith_short_16","alias_value":"ZF6EQNV2AFCWA5ZQ","created_at":"2026-07-05T08:51:18Z"},{"alias_kind":"pith_short_8","alias_value":"ZF6EQNV2","created_at":"2026-07-05T08:51:18Z"}],"graph_snapshots":[{"event_id":"sha256:34df62414dfaecc703fe0326c45ba7208ea831cc60d5616c8216a62129cd1ea6","target":"graph","created_at":"2026-07-05T08:51:18Z","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/2407.02604/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large vision language models (VLMs) have progressed incredibly from research to applicability for general-purpose use cases. LLaVA-Med, a pioneering large language and vision assistant for biomedicine, can perform multi-modal biomedical image and data analysis to provide a natural language interface for radiologists. While it is highly generalizable and works with multi-modal data, it is currently limited by well-known challenges that exist in the large language model space. Hallucinations and imprecision in responses can lead to misdiagnosis which currently hinder the clinical adaptability of","authors_text":"Abhijeet Parida, Hareem Nisar, Holger R. Roth, Marius George Linguraru, Ramon Sanchez-Jacob, Syed Muhammad Anwar, Vishwesh Nath, Zhifan Jiang","cross_cats":["cs.CL","cs.LG","eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-07-02T18:43:10Z","title":"D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02604","kind":"arxiv","version":2},"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":"sha256:0210c654f117ce4890ed89d452e07e3a3824d111c14db9eacd03c04e3e4c9184","target":"record","created_at":"2026-07-05T08:51:18Z","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":"5a8260d8b7f4cabfbc4cb0b36cd2baf4d6c7cfc480c2cf07ae974d346d2725c9","cross_cats_sorted":["cs.CL","cs.LG","eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-07-02T18:43:10Z","title_canon_sha256":"0bb9a083edf363b99e0d49a5c89f9517e619b0dabf9d8edc800e63cfe68478e0"},"schema_version":"1.0","source":{"id":"2407.02604","kind":"arxiv","version":2}},"canonical_sha256":"c97c4836ba01456077306a8d26ca2fca4e4b4b682dde5f0ce2670686002301ca","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c97c4836ba01456077306a8d26ca2fca4e4b4b682dde5f0ce2670686002301ca","first_computed_at":"2026-07-05T08:51:18.595256Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:51:18.595256Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"d2mPvehqWbYEdInGCgouXzMv1JdFK++WRbXrcJe/MlT2P+/Z+IxVrtCsNpYXGQI/+sHSV6nOmz9fi1OKnZjgCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:51:18.595642Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.02604","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0210c654f117ce4890ed89d452e07e3a3824d111c14db9eacd03c04e3e4c9184","sha256:34df62414dfaecc703fe0326c45ba7208ea831cc60d5616c8216a62129cd1ea6"],"state_sha256":"6686fca4de9b71e47c57934904ee09e8dda541615af0df9080cdbec464b2443d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g5guTwSltpL5MMseXwkxm4jjyAXj1NBDxwNEtxsOZ3WctqqLVca1tA6w0moS11K5WypBRqA6boyZIhKxhJqGAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T09:12:32.830075Z","bundle_sha256":"056aa2d8d439b04995422ffbd97725bccf2c6234dba2806f846e1afa52a23b78"}}