{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YIWXMRN2BFXP4IO33ISV2MUWLX","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":"2a55f3b619a0bf0060293b1bede2ba34ef653fe200f18843cbf0f9d9e5cce4be","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T12:28:40Z","title_canon_sha256":"1c69a85ccbf401099deffd5c2a55707fdb7d2219c028cb1afd9f094019fb9c2a"},"schema_version":"1.0","source":{"id":"2606.12006","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.12006","created_at":"2026-06-11T01:10:42Z"},{"alias_kind":"arxiv_version","alias_value":"2606.12006v1","created_at":"2026-06-11T01:10:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12006","created_at":"2026-06-11T01:10:42Z"},{"alias_kind":"pith_short_12","alias_value":"YIWXMRN2BFXP","created_at":"2026-06-11T01:10:42Z"},{"alias_kind":"pith_short_16","alias_value":"YIWXMRN2BFXP4IO3","created_at":"2026-06-11T01:10:42Z"},{"alias_kind":"pith_short_8","alias_value":"YIWXMRN2","created_at":"2026-06-11T01:10:42Z"}],"graph_snapshots":[{"event_id":"sha256:edc48ba7b7d83d88175bd5df02113ca9aa3053521d8b6c51d09910abcceead47","target":"graph","created_at":"2026-06-11T01:10:42Z","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/2606.12006/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis. While classical statistical and deep learning approaches have been widely studied, they typically require task-specific training and sufficient labeled data. Recent advances in tabular foundation models offer a new paradigm by learning general-purpose representations for structured data. However, their applicability to censored time-to-event prediction in clinical settings remains underexplored, as typical applications are restricted to discrete c","authors_text":"Alina Sirbu, Luca Cotugno, Marija Bezbradica, Martin Crane, Minh-Khoi Pham, Tai Tan Mai","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T12:28:40Z","title":"Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12006","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":"sha256:707c6d8f5cdfc94d7e06f9fb18063f174579d2ef3a85eccc0ff3a1bd28db0b60","target":"record","created_at":"2026-06-11T01:10:42Z","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":"2a55f3b619a0bf0060293b1bede2ba34ef653fe200f18843cbf0f9d9e5cce4be","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T12:28:40Z","title_canon_sha256":"1c69a85ccbf401099deffd5c2a55707fdb7d2219c028cb1afd9f094019fb9c2a"},"schema_version":"1.0","source":{"id":"2606.12006","kind":"arxiv","version":1}},"canonical_sha256":"c22d7645ba096efe21dbda255d32965de3d5e4acbe8b880b4e657105f980d380","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c22d7645ba096efe21dbda255d32965de3d5e4acbe8b880b4e657105f980d380","first_computed_at":"2026-06-11T01:10:42.364659Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T01:10:42.364659Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3vsDcczOYIlecGJIcTZolxXyWCR93sGDB3yf/eVIH3Fvl1SnEyEIHGsYz9jBZLFvEH/vcpwhmpmhVFsAK3EyCQ==","signature_status":"signed_v1","signed_at":"2026-06-11T01:10:42.365589Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.12006","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:707c6d8f5cdfc94d7e06f9fb18063f174579d2ef3a85eccc0ff3a1bd28db0b60","sha256:edc48ba7b7d83d88175bd5df02113ca9aa3053521d8b6c51d09910abcceead47"],"state_sha256":"d6aa6f035d69f06b99830be2adc968c7cd0d5c16b94dca3d181400f09f45cd38"}