{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:T2GHQJW2FM5CU4DWJMSCI32SBI","short_pith_number":"pith:T2GHQJW2","canonical_record":{"source":{"id":"2607.26628","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-29T08:54:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"468db77967d7c6c9e207944b799c64f0cde7cae215f51235e8b190d1aee1556b","abstract_canon_sha256":"c6e2e432df8e363c48eda888157c38009d478ea83aaf104f53cf827e692aeaa3"},"schema_version":"1.0"},"canonical_sha256":"9e8c7826da2b3a2a70764b24246f520a3f796b90514b6c169d758d1868a79cbd","source":{"kind":"arxiv","id":"2607.26628","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.26628","created_at":"2026-07-30T01:21:37Z"},{"alias_kind":"arxiv_version","alias_value":"2607.26628v1","created_at":"2026-07-30T01:21:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26628","created_at":"2026-07-30T01:21:37Z"},{"alias_kind":"pith_short_12","alias_value":"T2GHQJW2FM5C","created_at":"2026-07-30T01:21:37Z"},{"alias_kind":"pith_short_16","alias_value":"T2GHQJW2FM5CU4DW","created_at":"2026-07-30T01:21:37Z"},{"alias_kind":"pith_short_8","alias_value":"T2GHQJW2","created_at":"2026-07-30T01:21:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:T2GHQJW2FM5CU4DWJMSCI32SBI","target":"record","payload":{"canonical_record":{"source":{"id":"2607.26628","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-29T08:54:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"468db77967d7c6c9e207944b799c64f0cde7cae215f51235e8b190d1aee1556b","abstract_canon_sha256":"c6e2e432df8e363c48eda888157c38009d478ea83aaf104f53cf827e692aeaa3"},"schema_version":"1.0"},"canonical_sha256":"9e8c7826da2b3a2a70764b24246f520a3f796b90514b6c169d758d1868a79cbd","receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e8c7826da2b3a2a70764b24246f520a3f796b90514b6c169d758d1868a79cbd","last_reissued_at":"2026-07-30T01:21:37.374354Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:21:37.374354Z"},"source_kind":"arxiv","source_id":"2607.26628","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-30T01:21:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"le11hSOth26ZkH9411X0sexThavY5X6b64lt17P36YDESbSosrICQ+8NVcw/khLzyp6X1Lc7jWe9sxrIoUOpDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T08:02:20.801244Z"},"content_sha256":"4d9e9cdd54d698931918b27f999cf04c630e4b4a9437bdf2859b826480152656","schema_version":"1.0","event_id":"sha256:4d9e9cdd54d698931918b27f999cf04c630e4b4a9437bdf2859b826480152656"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:T2GHQJW2FM5CU4DWJMSCI32SBI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Understanding Context Sampling in TabPFN on Small Tabular Datasets","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Mohammed Abdullah","submitted_at":"2026-07-29T08:54:45Z","abstract_excerpt":"TabPFN performs classification through in-context learning: it conditions on a set of labeled training rows (the context, or prototypes) and predicts test labels without gradient updates. On small tabular datasets, practitioners must still choose the context size and which rows constitute the context. We study how these choices affect prediction stability, accuracy, and selection cost using repeated context sampling on 15 OpenML datasets. Specifically, we investigate (i) whether larger contexts reduce prediction variability across random draws, (ii) whether accuracy depends on preserving the t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26628","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/2607.26628/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":"2026-07-30T01:21:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xIJwfeYjqmlUm7RVg7KGWTsLUie8ubKXcPhmsY01YomouUcUdpGyGlh3Y3LCHyLjbtHr0eBTXuT0pbwTJ2gEAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T08:02:20.801823Z"},"content_sha256":"16090c93eb248b18238c54aa55690e93d6645a4ca4d8e83f6fd2e44c3af6c472","schema_version":"1.0","event_id":"sha256:16090c93eb248b18238c54aa55690e93d6645a4ca4d8e83f6fd2e44c3af6c472"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T2GHQJW2FM5CU4DWJMSCI32SBI/bundle.json","state_url":"https://pith.science/pith/T2GHQJW2FM5CU4DWJMSCI32SBI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T2GHQJW2FM5CU4DWJMSCI32SBI/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":"2026-08-06T08:02:20Z","links":{"resolver":"https://pith.science/pith/T2GHQJW2FM5CU4DWJMSCI32SBI","bundle":"https://pith.science/pith/T2GHQJW2FM5CU4DWJMSCI32SBI/bundle.json","state":"https://pith.science/pith/T2GHQJW2FM5CU4DWJMSCI32SBI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T2GHQJW2FM5CU4DWJMSCI32SBI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:T2GHQJW2FM5CU4DWJMSCI32SBI","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":"c6e2e432df8e363c48eda888157c38009d478ea83aaf104f53cf827e692aeaa3","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-29T08:54:45Z","title_canon_sha256":"468db77967d7c6c9e207944b799c64f0cde7cae215f51235e8b190d1aee1556b"},"schema_version":"1.0","source":{"id":"2607.26628","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.26628","created_at":"2026-07-30T01:21:37Z"},{"alias_kind":"arxiv_version","alias_value":"2607.26628v1","created_at":"2026-07-30T01:21:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26628","created_at":"2026-07-30T01:21:37Z"},{"alias_kind":"pith_short_12","alias_value":"T2GHQJW2FM5C","created_at":"2026-07-30T01:21:37Z"},{"alias_kind":"pith_short_16","alias_value":"T2GHQJW2FM5CU4DW","created_at":"2026-07-30T01:21:37Z"},{"alias_kind":"pith_short_8","alias_value":"T2GHQJW2","created_at":"2026-07-30T01:21:37Z"}],"graph_snapshots":[{"event_id":"sha256:16090c93eb248b18238c54aa55690e93d6645a4ca4d8e83f6fd2e44c3af6c472","target":"graph","created_at":"2026-07-30T01:21:37Z","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/2607.26628/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"TabPFN performs classification through in-context learning: it conditions on a set of labeled training rows (the context, or prototypes) and predicts test labels without gradient updates. On small tabular datasets, practitioners must still choose the context size and which rows constitute the context. We study how these choices affect prediction stability, accuracy, and selection cost using repeated context sampling on 15 OpenML datasets. Specifically, we investigate (i) whether larger contexts reduce prediction variability across random draws, (ii) whether accuracy depends on preserving the t","authors_text":"Mohammed Abdullah","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-29T08:54:45Z","title":"Understanding Context Sampling in TabPFN on Small Tabular Datasets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26628","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":"sha256:4d9e9cdd54d698931918b27f999cf04c630e4b4a9437bdf2859b826480152656","target":"record","created_at":"2026-07-30T01:21:37Z","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":"c6e2e432df8e363c48eda888157c38009d478ea83aaf104f53cf827e692aeaa3","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-29T08:54:45Z","title_canon_sha256":"468db77967d7c6c9e207944b799c64f0cde7cae215f51235e8b190d1aee1556b"},"schema_version":"1.0","source":{"id":"2607.26628","kind":"arxiv","version":1}},"canonical_sha256":"9e8c7826da2b3a2a70764b24246f520a3f796b90514b6c169d758d1868a79cbd","receipt":{"builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9e8c7826da2b3a2a70764b24246f520a3f796b90514b6c169d758d1868a79cbd","first_computed_at":"2026-07-30T01:21:37.374354Z","kind":"pith_receipt","last_reissued_at":"2026-07-30T01:21:37.374354Z","receipt_version":"0.3","signature_status":"unsigned_v0"},"source_id":"2607.26628","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d9e9cdd54d698931918b27f999cf04c630e4b4a9437bdf2859b826480152656","sha256:16090c93eb248b18238c54aa55690e93d6645a4ca4d8e83f6fd2e44c3af6c472"],"state_sha256":"7b1ee959aa6b87746789c6367fe199c7bb56f0a9be3b5176c93667e5902ad6ef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7V/oTYgMbofc0Ci/5apNg3zZN7plos4wkzjWWIcuk9dWQrN2co4xg4td0XrL52ZfTtxPA5vxUWLMoGNjoaWVCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T08:02:20.807876Z","bundle_sha256":"326df4e3c99a841fb28e5f3bf8fdcd08f83de670ae87529c869f9592c7c68274"}}