{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:3XIJOFR5UNRMQQIXTCEXVUBHQ7","short_pith_number":"pith:3XIJOFR5","canonical_record":{"source":{"id":"2607.27806","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T07:45:58Z","cross_cats_sorted":[],"title_canon_sha256":"8c36c1dca3dd86e090a34c533d9c17196958270e6c86f351f0255f061084a871","abstract_canon_sha256":"517f2a0cd2916efa236a812b11c0d9e0d7ec272aed1da3a5c126398f29ba89cd"},"schema_version":"1.0"},"canonical_sha256":"ddd097163da362c8411798897ad02787f959a15609c50a983a232930e4c186ba","source":{"kind":"arxiv","id":"2607.27806","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.27806","created_at":"2026-07-31T01:31:30Z"},{"alias_kind":"arxiv_version","alias_value":"2607.27806v1","created_at":"2026-07-31T01:31:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.27806","created_at":"2026-07-31T01:31:30Z"},{"alias_kind":"pith_short_12","alias_value":"3XIJOFR5UNRM","created_at":"2026-07-31T01:31:30Z"},{"alias_kind":"pith_short_16","alias_value":"3XIJOFR5UNRMQQIX","created_at":"2026-07-31T01:31:30Z"},{"alias_kind":"pith_short_8","alias_value":"3XIJOFR5","created_at":"2026-07-31T01:31:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:3XIJOFR5UNRMQQIXTCEXVUBHQ7","target":"record","payload":{"canonical_record":{"source":{"id":"2607.27806","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T07:45:58Z","cross_cats_sorted":[],"title_canon_sha256":"8c36c1dca3dd86e090a34c533d9c17196958270e6c86f351f0255f061084a871","abstract_canon_sha256":"517f2a0cd2916efa236a812b11c0d9e0d7ec272aed1da3a5c126398f29ba89cd"},"schema_version":"1.0"},"canonical_sha256":"ddd097163da362c8411798897ad02787f959a15609c50a983a232930e4c186ba","receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ddd097163da362c8411798897ad02787f959a15609c50a983a232930e4c186ba","last_reissued_at":"2026-07-31T01:31:30.235821Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:31:30.235821Z"},"source_kind":"arxiv","source_id":"2607.27806","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":"2026-07-31T01:31:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QCmzppnG0wWOV82VSRtgEctGCFIXnJhPLDhlJq8Utwo6orXBBz4g+IS2nb52tgFpgqSX8ei8ZCxVp30wMAa4AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T05:37:00.997265Z"},"content_sha256":"62025b9dd4474f842508edd8051a3f7ac40d8fa8bac05986cd93ce6d21f48518","schema_version":"1.0","event_id":"sha256:62025b9dd4474f842508edd8051a3f7ac40d8fa8bac05986cd93ce6d21f48518"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:3XIJOFR5UNRMQQIXTCEXVUBHQ7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LoMeVQA: A Comprehensive Benchmark for Longitudinal Medical VQA","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengmei Yang, Lianghua He, Longzhen Yang, Yihang Liu, Ying Wen, Zhangkai Ni, Zhilin Wu","submitted_at":"2026-07-30T07:45:58Z","abstract_excerpt":"In clinical practice, patients often undergo multiple imaging examinations over successive visits, yielding longitudinal data. Modeling such temporal information is crucial for reliable assessment of disease progression and treatment response. However, despite the rapid advancement of multimodal large language models (MLLMs), longitudinal medical visual reasoning remains largely underexplored. To fill this gap, we propose LoMeVQA, a comprehensive benchmark consisting of 206K longitudinal visual question answering (VQA) pairs for temporal medical image analysis. LoMeVQA covers five tasks: progr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.27806","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/2607.27806/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":"2026-07-31T01:31:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F738G/tH6ygenoTZEoI2ywexUfTjbcZhudtIZlT3MznIz4dui+jDle+JrisTbehQP9QPQ/mVIYt/eFFMRprABA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T05:37:00.998146Z"},"content_sha256":"3a742b3f2651e75b295050bf6434dc9be41129bbc44cb6962f7751aa69c6b699","schema_version":"1.0","event_id":"sha256:3a742b3f2651e75b295050bf6434dc9be41129bbc44cb6962f7751aa69c6b699"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3XIJOFR5UNRMQQIXTCEXVUBHQ7/bundle.json","state_url":"https://pith.science/pith/3XIJOFR5UNRMQQIXTCEXVUBHQ7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3XIJOFR5UNRMQQIXTCEXVUBHQ7/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":"2026-08-03T05:37:01Z","links":{"resolver":"https://pith.science/pith/3XIJOFR5UNRMQQIXTCEXVUBHQ7","bundle":"https://pith.science/pith/3XIJOFR5UNRMQQIXTCEXVUBHQ7/bundle.json","state":"https://pith.science/pith/3XIJOFR5UNRMQQIXTCEXVUBHQ7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3XIJOFR5UNRMQQIXTCEXVUBHQ7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:3XIJOFR5UNRMQQIXTCEXVUBHQ7","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":"517f2a0cd2916efa236a812b11c0d9e0d7ec272aed1da3a5c126398f29ba89cd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T07:45:58Z","title_canon_sha256":"8c36c1dca3dd86e090a34c533d9c17196958270e6c86f351f0255f061084a871"},"schema_version":"1.0","source":{"id":"2607.27806","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.27806","created_at":"2026-07-31T01:31:30Z"},{"alias_kind":"arxiv_version","alias_value":"2607.27806v1","created_at":"2026-07-31T01:31:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.27806","created_at":"2026-07-31T01:31:30Z"},{"alias_kind":"pith_short_12","alias_value":"3XIJOFR5UNRM","created_at":"2026-07-31T01:31:30Z"},{"alias_kind":"pith_short_16","alias_value":"3XIJOFR5UNRMQQIX","created_at":"2026-07-31T01:31:30Z"},{"alias_kind":"pith_short_8","alias_value":"3XIJOFR5","created_at":"2026-07-31T01:31:30Z"}],"graph_snapshots":[{"event_id":"sha256:3a742b3f2651e75b295050bf6434dc9be41129bbc44cb6962f7751aa69c6b699","target":"graph","created_at":"2026-07-31T01:31:30Z","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/2607.27806/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In clinical practice, patients often undergo multiple imaging examinations over successive visits, yielding longitudinal data. Modeling such temporal information is crucial for reliable assessment of disease progression and treatment response. However, despite the rapid advancement of multimodal large language models (MLLMs), longitudinal medical visual reasoning remains largely underexplored. To fill this gap, we propose LoMeVQA, a comprehensive benchmark consisting of 206K longitudinal visual question answering (VQA) pairs for temporal medical image analysis. LoMeVQA covers five tasks: progr","authors_text":"Chengmei Yang, Lianghua He, Longzhen Yang, Yihang Liu, Ying Wen, Zhangkai Ni, Zhilin Wu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T07:45:58Z","title":"LoMeVQA: A Comprehensive Benchmark for Longitudinal Medical VQA"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.27806","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":"sha256:62025b9dd4474f842508edd8051a3f7ac40d8fa8bac05986cd93ce6d21f48518","target":"record","created_at":"2026-07-31T01:31:30Z","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":"517f2a0cd2916efa236a812b11c0d9e0d7ec272aed1da3a5c126398f29ba89cd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T07:45:58Z","title_canon_sha256":"8c36c1dca3dd86e090a34c533d9c17196958270e6c86f351f0255f061084a871"},"schema_version":"1.0","source":{"id":"2607.27806","kind":"arxiv","version":1}},"canonical_sha256":"ddd097163da362c8411798897ad02787f959a15609c50a983a232930e4c186ba","receipt":{"builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ddd097163da362c8411798897ad02787f959a15609c50a983a232930e4c186ba","first_computed_at":"2026-07-31T01:31:30.235821Z","kind":"pith_receipt","last_reissued_at":"2026-07-31T01:31:30.235821Z","receipt_version":"0.3","signature_status":"unsigned_v0"},"source_id":"2607.27806","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:62025b9dd4474f842508edd8051a3f7ac40d8fa8bac05986cd93ce6d21f48518","sha256:3a742b3f2651e75b295050bf6434dc9be41129bbc44cb6962f7751aa69c6b699"],"state_sha256":"268d3fb87da239d9bece94ca010ce5f8b2281de198693c13b0c99a68b5cf0f71"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2UeOZzA6aE3hSNC8GYxzzCHgw5ryDhds7ytEx8IEaYG41Xm7QwZ+qnXG2Fq0sZHC2kOZJPN4adSDrX8c4yHcCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T05:37:01.003117Z","bundle_sha256":"cc61404b67bf4865e5f47d008b722ba6d8ddbc3ecc8dde04ac5b7da6e42ae470"}}