{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3UOJSXAIKVJMJLR5Y2GQ2GPNX3","short_pith_number":"pith:3UOJSXAI","schema_version":"1.0","canonical_sha256":"dd1c995c085552c4ae3dc68d0d19edbedcd60454a5204f0928f423e17c087a62","source":{"kind":"arxiv","id":"2607.26000","version":1},"attestation_state":"computed","paper":{"title":"Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"David Chushig-Muzo, Eva Milara, Felipe Grijalva, Luis Bote-Curiel, Luis Estrada-Petrocelli, Malena Loza","submitted_at":"2026-07-28T17:16:01Z","abstract_excerpt":"Tabular Foundation Models (TFMs) have emerged as novel approaches for tabular predictive tasks, demonstrating competitive predictive performance to ensemble tree-based models. Most TFMs are trained and evaluated on independent and identically distributed data, but this assumption changes in real-world scenarios due to distribution shifts, which compromise the robustness of models. Limited research has been conducted of TFMs under distribution shifts. We present an empirical evaluation of Out-Of-Distribution (OOD) performance of nine TFMs, spanning diverse pre-training strategies and architectu"},"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":"2607.26000","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-28T17:16:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"389a8d25d9fab943aab8ff0302d93a9d17fbf279047b9d43461eb4739245b8a1","abstract_canon_sha256":"ec523d8efb911fdbbbff3eecfe84f6b03a845ddc4de4efa4d9dae5f5796205b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-29T01:26:18.707443Z","signature_b64":"mLNZAXWNv5xUkOFhmBTX3igWPfKZMLYvRWUkOTB9342nuRWm/sNBIumbLwerSdMQerPd3srAes29ptFYcpkvBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd1c995c085552c4ae3dc68d0d19edbedcd60454a5204f0928f423e17c087a62","last_reissued_at":"2026-07-29T01:26:18.706589Z","signature_status":"signed_v1","first_computed_at":"2026-07-29T01:26:18.706589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"David Chushig-Muzo, Eva Milara, Felipe Grijalva, Luis Bote-Curiel, Luis Estrada-Petrocelli, Malena Loza","submitted_at":"2026-07-28T17:16:01Z","abstract_excerpt":"Tabular Foundation Models (TFMs) have emerged as novel approaches for tabular predictive tasks, demonstrating competitive predictive performance to ensemble tree-based models. Most TFMs are trained and evaluated on independent and identically distributed data, but this assumption changes in real-world scenarios due to distribution shifts, which compromise the robustness of models. Limited research has been conducted of TFMs under distribution shifts. We present an empirical evaluation of Out-Of-Distribution (OOD) performance of nine TFMs, spanning diverse pre-training strategies and architectu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26000","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/2607.26000/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":"2607.26000","created_at":"2026-07-29T01:26:18.707025+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.26000v1","created_at":"2026-07-29T01:26:18.707025+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26000","created_at":"2026-07-29T01:26:18.707025+00:00"},{"alias_kind":"pith_short_12","alias_value":"3UOJSXAIKVJM","created_at":"2026-07-29T01:26:18.707025+00:00"},{"alias_kind":"pith_short_16","alias_value":"3UOJSXAIKVJMJLR5","created_at":"2026-07-29T01:26:18.707025+00:00"},{"alias_kind":"pith_short_8","alias_value":"3UOJSXAI","created_at":"2026-07-29T01:26:18.707025+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/3UOJSXAIKVJMJLR5Y2GQ2GPNX3","json":"https://pith.science/pith/3UOJSXAIKVJMJLR5Y2GQ2GPNX3.json","graph_json":"https://pith.science/api/pith-number/3UOJSXAIKVJMJLR5Y2GQ2GPNX3/graph.json","events_json":"https://pith.science/api/pith-number/3UOJSXAIKVJMJLR5Y2GQ2GPNX3/events.json","paper":"https://pith.science/paper/3UOJSXAI"},"agent_actions":{"view_html":"https://pith.science/pith/3UOJSXAIKVJMJLR5Y2GQ2GPNX3","download_json":"https://pith.science/pith/3UOJSXAIKVJMJLR5Y2GQ2GPNX3.json","view_paper":"https://pith.science/paper/3UOJSXAI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.26000&json=true","fetch_graph":"https://pith.science/api/pith-number/3UOJSXAIKVJMJLR5Y2GQ2GPNX3/graph.json","fetch_events":"https://pith.science/api/pith-number/3UOJSXAIKVJMJLR5Y2GQ2GPNX3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3UOJSXAIKVJMJLR5Y2GQ2GPNX3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3UOJSXAIKVJMJLR5Y2GQ2GPNX3/action/storage_attestation","attest_author":"https://pith.science/pith/3UOJSXAIKVJMJLR5Y2GQ2GPNX3/action/author_attestation","sign_citation":"https://pith.science/pith/3UOJSXAIKVJMJLR5Y2GQ2GPNX3/action/citation_signature","submit_replication":"https://pith.science/pith/3UOJSXAIKVJMJLR5Y2GQ2GPNX3/action/replication_record"}},"created_at":"2026-07-29T01:26:18.707025+00:00","updated_at":"2026-07-29T01:26:18.707025+00:00"}