{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:2QS3QKBCS2AFJB62HJYJIALNMH","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":"febe35b03748559124321b71ce134e4f622dbdcf0080f049756350afd97f36a5","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-29T16:27:54Z","title_canon_sha256":"d89d0885e46b82468dc441848aa2c36cca907a9b2e14c182ff2ae4802edd5315"},"schema_version":"1.0","source":{"id":"2601.21941","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.21941","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"arxiv_version","alias_value":"2601.21941v1","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.21941","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"pith_short_12","alias_value":"2QS3QKBCS2AF","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"pith_short_16","alias_value":"2QS3QKBCS2AFJB62","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"pith_short_8","alias_value":"2QS3QKBC","created_at":"2026-05-20T00:02:08Z"}],"graph_snapshots":[{"event_id":"sha256:d1195fcd09089112bc2dcd24ebc92d91e31dc528842bf01020a0ada01f0a163f","target":"graph","created_at":"2026-05-20T00:02:08Z","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/2601.21941/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical multimodal representation learning aims to integrate heterogeneous data into unified patient representations to support clinical outcome prediction. However, real-world medical datasets commonly contain systematic biases from multiple sources, which poses significant challenges for medical multimodal representation learning. Existing approaches typically focus on effective multimodal fusion, neglecting inherent biased features that affect the generalization ability. To address these challenges, we propose a Dual-Stream Feature Decorrelation Framework that identifies and handles the bia","authors_text":"Haoyu Wang, Jing Liu, Lianlong Sun, Linxiao Gong, Xiaoguang Zhu, Yang Liu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-29T16:27:54Z","title":"Robust Multimodal Representation Learning in Healthcare"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.21941","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:b60520dbda11e2290c13a6ab3a9ea262bb891b63ebf32bf894803476a5772e55","target":"record","created_at":"2026-05-20T00:02:08Z","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":"febe35b03748559124321b71ce134e4f622dbdcf0080f049756350afd97f36a5","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-29T16:27:54Z","title_canon_sha256":"d89d0885e46b82468dc441848aa2c36cca907a9b2e14c182ff2ae4802edd5315"},"schema_version":"1.0","source":{"id":"2601.21941","kind":"arxiv","version":1}},"canonical_sha256":"d425b8282296805487da3a7094016d61c76d95f3fa6d6ec9b1498b983eb331e8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d425b8282296805487da3a7094016d61c76d95f3fa6d6ec9b1498b983eb331e8","first_computed_at":"2026-05-20T00:02:08.944736Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:02:08.944736Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ffIsohOxBFsVyOeXgd0aZBcQ3pG9vT5whNRDQZfTcVK4TY36+mAgPJFOxoE1Re9FqUZIj/grCISBiiSYFQjTBA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:02:08.945550Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.21941","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b60520dbda11e2290c13a6ab3a9ea262bb891b63ebf32bf894803476a5772e55","sha256:d1195fcd09089112bc2dcd24ebc92d91e31dc528842bf01020a0ada01f0a163f"],"state_sha256":"4a2d1c73c115a6449ca5dc56bb7ea401a14d451acdbd5d23f0ec14dd4beae06e"}