{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YZBXPWEAWSIHOM5SI5QHSUG4N2","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":"b844c0dd8d95cdc27daaff1f2dfadc21e88473406db612b93bb97f9dc9a72363","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-18T17:32:57Z","title_canon_sha256":"e2d28e2ac94be3099ca28486add619e50c099ab37fd949eb536826999ebe0d06"},"schema_version":"1.0","source":{"id":"2605.18696","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.18696","created_at":"2026-05-20T00:06:15Z"},{"alias_kind":"arxiv_version","alias_value":"2605.18696v1","created_at":"2026-05-20T00:06:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18696","created_at":"2026-05-20T00:06:15Z"},{"alias_kind":"pith_short_12","alias_value":"YZBXPWEAWSIH","created_at":"2026-05-20T00:06:15Z"},{"alias_kind":"pith_short_16","alias_value":"YZBXPWEAWSIHOM5S","created_at":"2026-05-20T00:06:15Z"},{"alias_kind":"pith_short_8","alias_value":"YZBXPWEA","created_at":"2026-05-20T00:06:15Z"}],"graph_snapshots":[{"event_id":"sha256:8ef2efabc1278620a419aaf3917b64caf29de633752a078d586179b71af465fb","target":"graph","created_at":"2026-05-20T00:06:15Z","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":[{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-20T00:01:59.086478Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.18696/integrity.json","findings":[],"snapshot_sha256":"90b41a3f7dc9284e8729b027683960c30267694e1e212bd091b5a7f46c8e927b","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Tabular foundation models (TFMs) now match or beat tuned gradient-boosted trees on a growing fraction of tabular tasks, but no single TFM wins on every dataset. Ensembling is the go to fix here, and it works less well than expected. Six modern TFMs form a near-redundant pool: their mean pairwise Q-statistic is $0.961$, close enough to $1$ that any convex combination is bounded above. We benchmark six ensemble strategies over six TFMs on 153 OpenML classification tasks. The best ensemble, two-level cascade stacking, buys $+0.18\\%$ accuracy over the strongest single TFM at $253\\times$ the comput","authors_text":"Aditya Tanna, Mohamed Bouadi, Nassim Bouarour, Pratinav Seth, Vinay Kumar Sankarapu, Yash Desai","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-18T17:32:57Z","title":"Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18696","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":"sha256:0b075d1dafa6a97424e218c3d82ba7f593bfa477bd3c2aeaf049eebd37c23cf5","target":"record","created_at":"2026-05-20T00:06:15Z","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":"b844c0dd8d95cdc27daaff1f2dfadc21e88473406db612b93bb97f9dc9a72363","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-18T17:32:57Z","title_canon_sha256":"e2d28e2ac94be3099ca28486add619e50c099ab37fd949eb536826999ebe0d06"},"schema_version":"1.0","source":{"id":"2605.18696","kind":"arxiv","version":1}},"canonical_sha256":"c64377d880b4907733b247607950dc6e9ca7e3108b84108fe420371fa30226b5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c64377d880b4907733b247607950dc6e9ca7e3108b84108fe420371fa30226b5","first_computed_at":"2026-05-20T00:06:15.517798Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:06:15.517798Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5aNdTYZpBu4Ix0RcRuXckUKVWwBgd5m7rLZPu9S89R5rIw0XXpis5UvMhkJc6+Dfc/LHK26ANkLdQyTWj0NCAw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:06:15.518784Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.18696","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0b075d1dafa6a97424e218c3d82ba7f593bfa477bd3c2aeaf049eebd37c23cf5","sha256:8ef2efabc1278620a419aaf3917b64caf29de633752a078d586179b71af465fb"],"state_sha256":"266624fa35a96214d8da2f19dca8bd76b252bd3414b95b790fbfb7002b388504"}