{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VMOMLD3YIQJLHVSGAUYIGLNBQY","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":"bb80210ab3bee973a4fa7d46b09a7d014afb1548d6d96a81003d377c1266b84f","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-28T07:50:00Z","title_canon_sha256":"9884a0305df4c41f820673608806b06833ffc390ea0d81eb19c598f96d6a7ac6"},"schema_version":"1.0","source":{"id":"2412.20070","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.20070","created_at":"2026-07-05T11:13:17Z"},{"alias_kind":"arxiv_version","alias_value":"2412.20070v2","created_at":"2026-07-05T11:13:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20070","created_at":"2026-07-05T11:13:17Z"},{"alias_kind":"pith_short_12","alias_value":"VMOMLD3YIQJL","created_at":"2026-07-05T11:13:17Z"},{"alias_kind":"pith_short_16","alias_value":"VMOMLD3YIQJLHVSG","created_at":"2026-07-05T11:13:17Z"},{"alias_kind":"pith_short_8","alias_value":"VMOMLD3Y","created_at":"2026-07-05T11:13:17Z"}],"graph_snapshots":[{"event_id":"sha256:2733a7810b4935b85927386c4257d9ce1a6028c5b7bdfa0ca4c4a408003651e0","target":"graph","created_at":"2026-07-05T11:13:17Z","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/2412.20070/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical imaging provides essential visual insights for diagnosis, and multimodal large language models (MLLMs) are increasingly utilized for its analysis due to their strong generalization capabilities; however, the underlying factors driving this generalization remain unclear. Current research suggests that multi-task training outperforms single-task as different tasks can benefit each other, but they often overlook the internal relationships within these tasks. To analyze this phenomenon, we attempted to employ compositional generalization (CG), which refers to the models' ability to underst","authors_text":"Benyou Wang, Dingjie Song, Junying Chen, Rongsheng Wang, Weihong Wang, Yize Chen, Yonglin Deng, Zhenyang Cai, Zixu Zhang","cross_cats":["cs.AI","cs.CL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-28T07:50:00Z","title":"Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20070","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":"sha256:4b5e739baef766c545b91c0ef5a4fefeed23f8acc7e566a27d0f7986ea36020f","target":"record","created_at":"2026-07-05T11:13:17Z","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":"bb80210ab3bee973a4fa7d46b09a7d014afb1548d6d96a81003d377c1266b84f","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-28T07:50:00Z","title_canon_sha256":"9884a0305df4c41f820673608806b06833ffc390ea0d81eb19c598f96d6a7ac6"},"schema_version":"1.0","source":{"id":"2412.20070","kind":"arxiv","version":2}},"canonical_sha256":"ab1cc58f784412b3d6460530832da1861a70c0c0fcdeb0ee81e97fc99a5ff4a2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ab1cc58f784412b3d6460530832da1861a70c0c0fcdeb0ee81e97fc99a5ff4a2","first_computed_at":"2026-07-05T11:13:17.864469Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:13:17.864469Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yeaHRx2EMcuxsKug0JS9qvmHktRxexMb1QvJlSkJpd0pK8iMOjc+YdCfnb89MhzbZDJ4ZE1VB+SAOwLUbZOgBg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:13:17.864966Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.20070","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4b5e739baef766c545b91c0ef5a4fefeed23f8acc7e566a27d0f7986ea36020f","sha256:2733a7810b4935b85927386c4257d9ce1a6028c5b7bdfa0ca4c4a408003651e0"],"state_sha256":"c7d01223171d78aff943209399ccae2d000f4c4659139c158bc450c51ca49c24"}