{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:O6FML2FCUFMCPXYUUJXAJJMRIJ","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":"ba22ce3a46590e680f1fd0318809ed405d5c8d24af07400a87f0359bd00a2700","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T22:07:04Z","title_canon_sha256":"b64582a08f1782c6c7ea7cb32d7717ef80b5c8620d919b79a96c8e0ab994217c"},"schema_version":"1.0","source":{"id":"2606.11473","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11473","created_at":"2026-06-11T01:09:50Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11473v1","created_at":"2026-06-11T01:09:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11473","created_at":"2026-06-11T01:09:50Z"},{"alias_kind":"pith_short_12","alias_value":"O6FML2FCUFMC","created_at":"2026-06-11T01:09:50Z"},{"alias_kind":"pith_short_16","alias_value":"O6FML2FCUFMCPXYU","created_at":"2026-06-11T01:09:50Z"},{"alias_kind":"pith_short_8","alias_value":"O6FML2FC","created_at":"2026-06-11T01:09:50Z"}],"graph_snapshots":[{"event_id":"sha256:ca9e7050cd2e74bd571059d56071e3c6ea7d7427e8c559604523977cf6cb0ebf","target":"graph","created_at":"2026-06-11T01:09:50Z","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/2606.11473/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prior-fitted networks (PFNs) are a promising class of tabular foundation models that perform in-context learning, whereby the entire labelled training set is supplied as context, and predictions for test queries are produced in a single forward pass. However, the quadratically scaling self-attention mechanism in many PFN architectures makes inference prohibitive for very large training datasets. We propose CRUMB (Clustered Retrieval Using Minimised-MMD Batching), a three-stage inference wrapper that (i) clusters the test queries, (ii) selects a small, distributionally matched training subset f","authors_text":"Akshay Seshadri, Jamie Heredge, Mattia J. Villani, Niraj Kumar, Pranav Deshpande","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T22:07:04Z","title":"CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11473","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:f3127a1b74344730f25675b5a06b16456434f85dfd8898c75c0f35ce6adfa73f","target":"record","created_at":"2026-06-11T01:09:50Z","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":"ba22ce3a46590e680f1fd0318809ed405d5c8d24af07400a87f0359bd00a2700","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T22:07:04Z","title_canon_sha256":"b64582a08f1782c6c7ea7cb32d7717ef80b5c8620d919b79a96c8e0ab994217c"},"schema_version":"1.0","source":{"id":"2606.11473","kind":"arxiv","version":1}},"canonical_sha256":"778ac5e8a2a15827df14a26e04a5914279ab63a8201dfee09bd9fd9fd1ae455d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"778ac5e8a2a15827df14a26e04a5914279ab63a8201dfee09bd9fd9fd1ae455d","first_computed_at":"2026-06-11T01:09:50.999232Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T01:09:50.999232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+ou1So2IKJMOyo9giVTFILVqoLZzfsHgY9jJfSRrG7oySG27emhL2fwGuBBk0jMfJA3lAqjklKMKh4op6MMQBw==","signature_status":"signed_v1","signed_at":"2026-06-11T01:09:50.999888Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.11473","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3127a1b74344730f25675b5a06b16456434f85dfd8898c75c0f35ce6adfa73f","sha256:ca9e7050cd2e74bd571059d56071e3c6ea7d7427e8c559604523977cf6cb0ebf"],"state_sha256":"7af7e14b595e46672afe452239ac03fbd8743db810af5187f40acc3d8fde9146"}