{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5VCWECKVZJLO444GCHSXWDFXAE","short_pith_number":"pith:5VCWECKV","schema_version":"1.0","canonical_sha256":"ed45620955ca56ee738611e57b0cb7013b707881be45886ce71414131e3f1117","source":{"kind":"arxiv","id":"2608.12592","version":1},"attestation_state":"computed","paper":{"title":"Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haochen Zhang, Jiaheng Guo, Nicholas Knoz, Tianlong Chen, Yu-Chao Huang","submitted_at":"2026-08-12T21:04:44Z","abstract_excerpt":"Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patient. Conditional generation offers a remedy: an absent signal can be synthesized from co-recorded signals and routine clinical variables. Existing generators, however, are built around a single conditioning modality and degrade when forced to handle the heterogeneous, irregularly missing mix of time-variant signals and static covariates seen in practice. We propose ReCoGen (Represent Conditions, then Generate), a two-s"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2608.12592","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-08-12T21:04:44Z","cross_cats_sorted":[],"title_canon_sha256":"3646198f83a8d987372e4816cc8df04faf0bf6d60ecd99053eb83a8c51cdfabe","abstract_canon_sha256":"79fd75f79e747168e995bec6ebd511cac9ae0df422486dbc970d1fec7770c485"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-14T00:45:32.882426Z","signature_b64":"8uRZ/GC16lKiJyaFacM4xmuHImSjgBPuCqAHjhXxkFlWjYNYnl27oVtlwgpyKp1UlSwLMp6QhxUNzeC9u97dDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ed45620955ca56ee738611e57b0cb7013b707881be45886ce71414131e3f1117","last_reissued_at":"2026-08-14T00:45:32.875940Z","signature_status":"signed_v1","first_computed_at":"2026-08-14T00:45:32.875940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haochen Zhang, Jiaheng Guo, Nicholas Knoz, Tianlong Chen, Yu-Chao Huang","submitted_at":"2026-08-12T21:04:44Z","abstract_excerpt":"Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patient. Conditional generation offers a remedy: an absent signal can be synthesized from co-recorded signals and routine clinical variables. Existing generators, however, are built around a single conditioning modality and degrade when forced to handle the heterogeneous, irregularly missing mix of time-variant signals and static covariates seen in practice. We propose ReCoGen (Represent Conditions, then Generate), a two-s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.12592","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/2608.12592/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2608.12592","created_at":"2026-08-14T00:45:32.880705+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.12592v1","created_at":"2026-08-14T00:45:32.880705+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.12592","created_at":"2026-08-14T00:45:32.880705+00:00"},{"alias_kind":"pith_short_12","alias_value":"5VCWECKVZJLO","created_at":"2026-08-14T00:45:32.880705+00:00"},{"alias_kind":"pith_short_16","alias_value":"5VCWECKVZJLO444G","created_at":"2026-08-14T00:45:32.880705+00:00"},{"alias_kind":"pith_short_8","alias_value":"5VCWECKV","created_at":"2026-08-14T00:45:32.880705+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5VCWECKVZJLO444GCHSXWDFXAE","json":"https://pith.science/pith/5VCWECKVZJLO444GCHSXWDFXAE.json","graph_json":"https://pith.science/api/pith-number/5VCWECKVZJLO444GCHSXWDFXAE/graph.json","events_json":"https://pith.science/api/pith-number/5VCWECKVZJLO444GCHSXWDFXAE/events.json","paper":"https://pith.science/paper/5VCWECKV"},"agent_actions":{"view_html":"https://pith.science/pith/5VCWECKVZJLO444GCHSXWDFXAE","download_json":"https://pith.science/pith/5VCWECKVZJLO444GCHSXWDFXAE.json","view_paper":"https://pith.science/paper/5VCWECKV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.12592&json=true","fetch_graph":"https://pith.science/api/pith-number/5VCWECKVZJLO444GCHSXWDFXAE/graph.json","fetch_events":"https://pith.science/api/pith-number/5VCWECKVZJLO444GCHSXWDFXAE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5VCWECKVZJLO444GCHSXWDFXAE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5VCWECKVZJLO444GCHSXWDFXAE/action/storage_attestation","attest_author":"https://pith.science/pith/5VCWECKVZJLO444GCHSXWDFXAE/action/author_attestation","sign_citation":"https://pith.science/pith/5VCWECKVZJLO444GCHSXWDFXAE/action/citation_signature","submit_replication":"https://pith.science/pith/5VCWECKVZJLO444GCHSXWDFXAE/action/replication_record"}},"created_at":"2026-08-14T00:45:32.880705+00:00","updated_at":"2026-08-14T00:45:32.880705+00:00"}