{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NXXFSHQLSQYZCNHZFINXVHSU4B","short_pith_number":"pith:NXXFSHQL","schema_version":"1.0","canonical_sha256":"6dee591e0b94319134f92a1b7a9e54e05dfd9c6fb084aada876b8aeb5c131f67","source":{"kind":"arxiv","id":"2506.21387","version":2},"attestation_state":"computed","paper":{"title":"Early Stopping Tabular In-Context Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Frank Hutter, Jaris K\\\"uken, Lennart Purucker","submitted_at":"2025-06-26T15:36:37Z","abstract_excerpt":"Tabular foundation models have shown strong performance across various tabular learning tasks via in-context learning, offering robust generalization without any downstream finetuning. However, their inference-time costs remain high, particularly for larger datasets. To address this, we propose early-stopping the in-context learning process. We achieve this by dynamically evaluating whether to stop in-context learning after each Transformer encoder layer. Once stopped, we decode the embedding using a pre-trained layer-wise decoder. Experiments across 34 small classification tasks size show tha"},"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":"2506.21387","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-26T15:36:37Z","cross_cats_sorted":[],"title_canon_sha256":"cdf90d679eee0cc3742c217319f3b66ae477023d1f96ae9f3b51a1175281f5d2","abstract_canon_sha256":"a81a88a75f05b03ef9adf177086698d5a587977aa482c9dcc069a2fa14f55fd7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:28:38.391632Z","signature_b64":"xXfw2TPvW85sOQ2MVc/TpTGhBlpfJB4myp7kxS1xtpwOBVoB6GmeNOu87A9/pXwaJ08KMsK71K4C3hnLyYdYCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6dee591e0b94319134f92a1b7a9e54e05dfd9c6fb084aada876b8aeb5c131f67","last_reissued_at":"2026-07-05T11:28:38.391142Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:28:38.391142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Early Stopping Tabular In-Context Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Frank Hutter, Jaris K\\\"uken, Lennart Purucker","submitted_at":"2025-06-26T15:36:37Z","abstract_excerpt":"Tabular foundation models have shown strong performance across various tabular learning tasks via in-context learning, offering robust generalization without any downstream finetuning. However, their inference-time costs remain high, particularly for larger datasets. To address this, we propose early-stopping the in-context learning process. We achieve this by dynamically evaluating whether to stop in-context learning after each Transformer encoder layer. Once stopped, we decode the embedding using a pre-trained layer-wise decoder. Experiments across 34 small classification tasks size show tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21387","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/2506.21387/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":"2506.21387","created_at":"2026-07-05T11:28:38.391203+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.21387v2","created_at":"2026-07-05T11:28:38.391203+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21387","created_at":"2026-07-05T11:28:38.391203+00:00"},{"alias_kind":"pith_short_12","alias_value":"NXXFSHQLSQYZ","created_at":"2026-07-05T11:28:38.391203+00:00"},{"alias_kind":"pith_short_16","alias_value":"NXXFSHQLSQYZCNHZ","created_at":"2026-07-05T11:28:38.391203+00:00"},{"alias_kind":"pith_short_8","alias_value":"NXXFSHQL","created_at":"2026-07-05T11:28:38.391203+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07345","citing_title":"TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31272","citing_title":"Algorithmic Recourse of In-Context Learning for Tabular Data","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NXXFSHQLSQYZCNHZFINXVHSU4B","json":"https://pith.science/pith/NXXFSHQLSQYZCNHZFINXVHSU4B.json","graph_json":"https://pith.science/api/pith-number/NXXFSHQLSQYZCNHZFINXVHSU4B/graph.json","events_json":"https://pith.science/api/pith-number/NXXFSHQLSQYZCNHZFINXVHSU4B/events.json","paper":"https://pith.science/paper/NXXFSHQL"},"agent_actions":{"view_html":"https://pith.science/pith/NXXFSHQLSQYZCNHZFINXVHSU4B","download_json":"https://pith.science/pith/NXXFSHQLSQYZCNHZFINXVHSU4B.json","view_paper":"https://pith.science/paper/NXXFSHQL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.21387&json=true","fetch_graph":"https://pith.science/api/pith-number/NXXFSHQLSQYZCNHZFINXVHSU4B/graph.json","fetch_events":"https://pith.science/api/pith-number/NXXFSHQLSQYZCNHZFINXVHSU4B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NXXFSHQLSQYZCNHZFINXVHSU4B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NXXFSHQLSQYZCNHZFINXVHSU4B/action/storage_attestation","attest_author":"https://pith.science/pith/NXXFSHQLSQYZCNHZFINXVHSU4B/action/author_attestation","sign_citation":"https://pith.science/pith/NXXFSHQLSQYZCNHZFINXVHSU4B/action/citation_signature","submit_replication":"https://pith.science/pith/NXXFSHQLSQYZCNHZFINXVHSU4B/action/replication_record"}},"created_at":"2026-07-05T11:28:38.391203+00:00","updated_at":"2026-07-05T11:28:38.391203+00:00"}