{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NMGNSFAX3BTVDLLG7AVILOKKWT","short_pith_number":"pith:NMGNSFAX","schema_version":"1.0","canonical_sha256":"6b0cd91417d86751ad66f82a85b94ab4c644ec5046c56fcb61c1954e4467449b","source":{"kind":"arxiv","id":"2404.18219","version":1},"attestation_state":"computed","paper":{"title":"BUFF: Boosted Decision Tree based Ultra-Fast Flow matching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","hep-ex","hep-ph","physics.data-an"],"primary_cat":"physics.ins-det","authors_text":"Cheng Jiang, Huilin Qu, Sitian Qian","submitted_at":"2024-04-28T15:31:20Z","abstract_excerpt":"Tabular data stands out as one of the most frequently encountered types in high energy physics. Unlike commonly homogeneous data such as pixelated images, simulating high-dimensional tabular data and accurately capturing their correlations are often quite challenging, even with the most advanced architectures. Based on the findings that tree-based models surpass the performance of deep learning models for tasks specific to tabular data, we adopt the very recent generative modeling class named conditional flow matching and employ different techniques to integrate the usage of Gradient Boosted T"},"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":"2404.18219","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ins-det","submitted_at":"2024-04-28T15:31:20Z","cross_cats_sorted":["cs.LG","hep-ex","hep-ph","physics.data-an"],"title_canon_sha256":"801a4af67d60dc388f75bec51f9906f4e38543360f80d727ce1810f90111981e","abstract_canon_sha256":"8793567bb70d814fddd2cace444c8f4f04ffa6b369d5e46dbef53c1085ec21e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:13:00.062630Z","signature_b64":"wzeDtlD/Q+SqpUHKjTDtnDddCWnS7COBkKSUFD0eSnm9zXhqBntVRimw7R3yhHJ0JyD64uNlDYmVsIR+FuKVCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b0cd91417d86751ad66f82a85b94ab4c644ec5046c56fcb61c1954e4467449b","last_reissued_at":"2026-07-05T08:13:00.062158Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:13:00.062158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BUFF: Boosted Decision Tree based Ultra-Fast Flow matching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","hep-ex","hep-ph","physics.data-an"],"primary_cat":"physics.ins-det","authors_text":"Cheng Jiang, Huilin Qu, Sitian Qian","submitted_at":"2024-04-28T15:31:20Z","abstract_excerpt":"Tabular data stands out as one of the most frequently encountered types in high energy physics. Unlike commonly homogeneous data such as pixelated images, simulating high-dimensional tabular data and accurately capturing their correlations are often quite challenging, even with the most advanced architectures. Based on the findings that tree-based models surpass the performance of deep learning models for tasks specific to tabular data, we adopt the very recent generative modeling class named conditional flow matching and employ different techniques to integrate the usage of Gradient Boosted T"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.18219","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/2404.18219/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":"2404.18219","created_at":"2026-07-05T08:13:00.062211+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.18219v1","created_at":"2026-07-05T08:13:00.062211+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.18219","created_at":"2026-07-05T08:13:00.062211+00:00"},{"alias_kind":"pith_short_12","alias_value":"NMGNSFAX3BTV","created_at":"2026-07-05T08:13:00.062211+00:00"},{"alias_kind":"pith_short_16","alias_value":"NMGNSFAX3BTVDLLG","created_at":"2026-07-05T08:13:00.062211+00:00"},{"alias_kind":"pith_short_8","alias_value":"NMGNSFAX","created_at":"2026-07-05T08:13:00.062211+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11304","citing_title":"SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2606.04165","citing_title":"CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters","ref_index":51,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NMGNSFAX3BTVDLLG7AVILOKKWT","json":"https://pith.science/pith/NMGNSFAX3BTVDLLG7AVILOKKWT.json","graph_json":"https://pith.science/api/pith-number/NMGNSFAX3BTVDLLG7AVILOKKWT/graph.json","events_json":"https://pith.science/api/pith-number/NMGNSFAX3BTVDLLG7AVILOKKWT/events.json","paper":"https://pith.science/paper/NMGNSFAX"},"agent_actions":{"view_html":"https://pith.science/pith/NMGNSFAX3BTVDLLG7AVILOKKWT","download_json":"https://pith.science/pith/NMGNSFAX3BTVDLLG7AVILOKKWT.json","view_paper":"https://pith.science/paper/NMGNSFAX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.18219&json=true","fetch_graph":"https://pith.science/api/pith-number/NMGNSFAX3BTVDLLG7AVILOKKWT/graph.json","fetch_events":"https://pith.science/api/pith-number/NMGNSFAX3BTVDLLG7AVILOKKWT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NMGNSFAX3BTVDLLG7AVILOKKWT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NMGNSFAX3BTVDLLG7AVILOKKWT/action/storage_attestation","attest_author":"https://pith.science/pith/NMGNSFAX3BTVDLLG7AVILOKKWT/action/author_attestation","sign_citation":"https://pith.science/pith/NMGNSFAX3BTVDLLG7AVILOKKWT/action/citation_signature","submit_replication":"https://pith.science/pith/NMGNSFAX3BTVDLLG7AVILOKKWT/action/replication_record"}},"created_at":"2026-07-05T08:13:00.062211+00:00","updated_at":"2026-07-05T08:13:00.062211+00:00"}