{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YF5SB6PXUR4YVIH2PMLLL76CEI","short_pith_number":"pith:YF5SB6PX","canonical_record":{"source":{"id":"2502.05564","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-08T13:25:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9f3c86a8b09784adadeb3766543ce88b62282a357696304d9a812d686552ff56","abstract_canon_sha256":"3001da2d8266ffd26f8b92abc8479332bafea035a8e29472c0503766a72752b6"},"schema_version":"1.0"},"canonical_sha256":"c17b20f9f7a4798aa0fa7b16b5ffc222289fb7c1496212e20fa503c7529317a8","source":{"kind":"arxiv","id":"2502.05564","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05564","created_at":"2026-05-20T13:27:29Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05564v2","created_at":"2026-05-20T13:27:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05564","created_at":"2026-05-20T13:27:29Z"},{"alias_kind":"pith_short_12","alias_value":"YF5SB6PXUR4Y","created_at":"2026-05-20T13:27:29Z"},{"alias_kind":"pith_short_16","alias_value":"YF5SB6PXUR4YVIH2","created_at":"2026-05-20T13:27:29Z"},{"alias_kind":"pith_short_8","alias_value":"YF5SB6PX","created_at":"2026-05-20T13:27:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YF5SB6PXUR4YVIH2PMLLL76CEI","target":"record","payload":{"canonical_record":{"source":{"id":"2502.05564","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-08T13:25:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9f3c86a8b09784adadeb3766543ce88b62282a357696304d9a812d686552ff56","abstract_canon_sha256":"3001da2d8266ffd26f8b92abc8479332bafea035a8e29472c0503766a72752b6"},"schema_version":"1.0"},"canonical_sha256":"c17b20f9f7a4798aa0fa7b16b5ffc222289fb7c1496212e20fa503c7529317a8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T13:27:29.568226Z","signature_b64":"nn8KilLqEVd85ZgBzmUdR8isdqQ4Jca63SF0HKo+b1VF+iyOwqvSG5p9/aGhVm02iB0Dyc07EP6x3PaCsNQMDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c17b20f9f7a4798aa0fa7b16b5ffc222289fb7c1496212e20fa503c7529317a8","last_reissued_at":"2026-05-20T13:27:29.564638Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T13:27:29.564638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.05564","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T13:27:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7xDkyMiuIs9BiKBKgtXkHHBw+IwMryVPp2qKqu7pcXnMvdkbwH7edZUsD8V3ngbHwD7mehHQUUzEUweyRh2jCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T08:22:00.244226Z"},"content_sha256":"4e0738f5a5ccc37abaffacfb63832d9d8a87c19c20aedffd32bab6b25f03c751","schema_version":"1.0","event_id":"sha256:4e0738f5a5ccc37abaffacfb63832d9d8a87c19c20aedffd32bab6b25f03c751"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YF5SB6PXUR4YVIH2PMLLL76CEI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"David Holzm\\\"uller, Ga\\\"el Varoquaux, Jingang Qu, Marine Le Morvan","submitted_at":"2025-02-08T13:25:04Z","abstract_excerpt":"The long-standing dominance of gradient-boosted decision trees on tabular data is currently challenged by tabular foundation models using In-Context Learning (ICL): setting the training data as context for the test data and predicting in a single forward pass without parameter updates. While TabPFNv2 foundation model excels on tables with up to 10K samples, its alternating column- and row-wise attentions make handling large training sets computationally prohibitive. So, can ICL be effectively scaled and deliver a benefit for larger tables? We introduce TabICL, a tabular foundation model for cl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05564","kind":"arxiv","version":2},"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/2502.05564/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T13:27:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P4HyScU7GYhy4UNiAhCsBU7fiUOYRE308ZKbuvubsGsXf6mS0ZwluWJLXDZjKctAfgNy8m8ne+7z6tE31Il3AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T08:22:00.244628Z"},"content_sha256":"ff13fb86d07b744c81780040d002fb1bd64a3b4a44f660f379585ebd13a2fe93","schema_version":"1.0","event_id":"sha256:ff13fb86d07b744c81780040d002fb1bd64a3b4a44f660f379585ebd13a2fe93"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YF5SB6PXUR4YVIH2PMLLL76CEI/bundle.json","state_url":"https://pith.science/pith/YF5SB6PXUR4YVIH2PMLLL76CEI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YF5SB6PXUR4YVIH2PMLLL76CEI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-21T08:22:00Z","links":{"resolver":"https://pith.science/pith/YF5SB6PXUR4YVIH2PMLLL76CEI","bundle":"https://pith.science/pith/YF5SB6PXUR4YVIH2PMLLL76CEI/bundle.json","state":"https://pith.science/pith/YF5SB6PXUR4YVIH2PMLLL76CEI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YF5SB6PXUR4YVIH2PMLLL76CEI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YF5SB6PXUR4YVIH2PMLLL76CEI","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":"3001da2d8266ffd26f8b92abc8479332bafea035a8e29472c0503766a72752b6","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-08T13:25:04Z","title_canon_sha256":"9f3c86a8b09784adadeb3766543ce88b62282a357696304d9a812d686552ff56"},"schema_version":"1.0","source":{"id":"2502.05564","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05564","created_at":"2026-05-20T13:27:29Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05564v2","created_at":"2026-05-20T13:27:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05564","created_at":"2026-05-20T13:27:29Z"},{"alias_kind":"pith_short_12","alias_value":"YF5SB6PXUR4Y","created_at":"2026-05-20T13:27:29Z"},{"alias_kind":"pith_short_16","alias_value":"YF5SB6PXUR4YVIH2","created_at":"2026-05-20T13:27:29Z"},{"alias_kind":"pith_short_8","alias_value":"YF5SB6PX","created_at":"2026-05-20T13:27:29Z"}],"graph_snapshots":[{"event_id":"sha256:ff13fb86d07b744c81780040d002fb1bd64a3b4a44f660f379585ebd13a2fe93","target":"graph","created_at":"2026-05-20T13:27:29Z","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/2502.05564/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The long-standing dominance of gradient-boosted decision trees on tabular data is currently challenged by tabular foundation models using In-Context Learning (ICL): setting the training data as context for the test data and predicting in a single forward pass without parameter updates. While TabPFNv2 foundation model excels on tables with up to 10K samples, its alternating column- and row-wise attentions make handling large training sets computationally prohibitive. So, can ICL be effectively scaled and deliver a benefit for larger tables? We introduce TabICL, a tabular foundation model for cl","authors_text":"David Holzm\\\"uller, Ga\\\"el Varoquaux, Jingang Qu, Marine Le Morvan","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-08T13:25:04Z","title":"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05564","kind":"arxiv","version":2},"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":"sha256:4e0738f5a5ccc37abaffacfb63832d9d8a87c19c20aedffd32bab6b25f03c751","target":"record","created_at":"2026-05-20T13:27:29Z","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":"3001da2d8266ffd26f8b92abc8479332bafea035a8e29472c0503766a72752b6","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-08T13:25:04Z","title_canon_sha256":"9f3c86a8b09784adadeb3766543ce88b62282a357696304d9a812d686552ff56"},"schema_version":"1.0","source":{"id":"2502.05564","kind":"arxiv","version":2}},"canonical_sha256":"c17b20f9f7a4798aa0fa7b16b5ffc222289fb7c1496212e20fa503c7529317a8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c17b20f9f7a4798aa0fa7b16b5ffc222289fb7c1496212e20fa503c7529317a8","first_computed_at":"2026-05-20T13:27:29.564638Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T13:27:29.564638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nn8KilLqEVd85ZgBzmUdR8isdqQ4Jca63SF0HKo+b1VF+iyOwqvSG5p9/aGhVm02iB0Dyc07EP6x3PaCsNQMDg==","signature_status":"signed_v1","signed_at":"2026-05-20T13:27:29.568226Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.05564","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4e0738f5a5ccc37abaffacfb63832d9d8a87c19c20aedffd32bab6b25f03c751","sha256:ff13fb86d07b744c81780040d002fb1bd64a3b4a44f660f379585ebd13a2fe93"],"state_sha256":"195e6eaf8af32c5bf83c961be78ccf601d3886ca7afcb2a1eda93e2caa68bddc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H9L8ggZd24QeWTYUvZENfQPqodsvs0TM6ErxDZEY+z+TXm/iKuPshIXsSo4FfL2ZbTNdfiLrGlwF1cQ1bg/xDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T08:22:00.247237Z","bundle_sha256":"f179cca74335f9ce3d2c36d4080db7f0f2c435016c2e28557f78d267dfb3b6b0"}}