{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:H3RH3TILIQBH2G425AF5J4TAM2","short_pith_number":"pith:H3RH3TIL","schema_version":"1.0","canonical_sha256":"3ee27dcd0b44027d1b9ae80bd4f26066979391a57c3a933d69cd74f1f3e711b7","source":{"kind":"arxiv","id":"2606.24140","version":1},"attestation_state":"computed","paper":{"title":"A Time-Reparameterized Cumulative Intensity Extrapolation Sampler for Discrete Flow Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Feiyang Fu, Hehe Fan","submitted_at":"2026-06-23T04:48:45Z","abstract_excerpt":"Discrete flow matching (DFM) provides a principled framework for generative modeling on discrete state spaces via continuous-time Markov chain dynamics. In practice, sampling for DFM commonly employs discretizations such as $\\tau$-leaping, yet efficient sampling methods under a limited number of function evaluations (NFE) remain less studied. To address this gap, we propose the Time-Reparameterized Cumulative Intensity Extrapolation (TR-CIE) sampler, which aims to improve sampling quality when function evaluations are restricted. TR-CIE consists of two components. First, a schedule-based time "},"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":"2606.24140","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-23T04:48:45Z","cross_cats_sorted":[],"title_canon_sha256":"71ecb783366ba43f6e580f04d8ce4797cb9b7b7abd48534af2f9e11cd8988a72","abstract_canon_sha256":"8d0919e9dbc5d0512470ee9020d8fab6bfe9d7c38c81c875e04b516825bee1ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:14:42.081671Z","signature_b64":"8+nyC+gQbAYFX/+GFmMG8nqv1WU3dAssNZg5809IC5vDQbd3aJyLkl0pF+lOmpx0a3eLcW6f2TQuSpsSVxbUAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ee27dcd0b44027d1b9ae80bd4f26066979391a57c3a933d69cd74f1f3e711b7","last_reissued_at":"2026-06-24T01:14:42.081328Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:14:42.081328Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Time-Reparameterized Cumulative Intensity Extrapolation Sampler for Discrete Flow Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Feiyang Fu, Hehe Fan","submitted_at":"2026-06-23T04:48:45Z","abstract_excerpt":"Discrete flow matching (DFM) provides a principled framework for generative modeling on discrete state spaces via continuous-time Markov chain dynamics. In practice, sampling for DFM commonly employs discretizations such as $\\tau$-leaping, yet efficient sampling methods under a limited number of function evaluations (NFE) remain less studied. To address this gap, we propose the Time-Reparameterized Cumulative Intensity Extrapolation (TR-CIE) sampler, which aims to improve sampling quality when function evaluations are restricted. TR-CIE consists of two components. First, a schedule-based time "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24140","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/2606.24140/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":"2606.24140","created_at":"2026-06-24T01:14:42.081389+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.24140v1","created_at":"2026-06-24T01:14:42.081389+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24140","created_at":"2026-06-24T01:14:42.081389+00:00"},{"alias_kind":"pith_short_12","alias_value":"H3RH3TILIQBH","created_at":"2026-06-24T01:14:42.081389+00:00"},{"alias_kind":"pith_short_16","alias_value":"H3RH3TILIQBH2G42","created_at":"2026-06-24T01:14:42.081389+00:00"},{"alias_kind":"pith_short_8","alias_value":"H3RH3TIL","created_at":"2026-06-24T01:14:42.081389+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H3RH3TILIQBH2G425AF5J4TAM2","json":"https://pith.science/pith/H3RH3TILIQBH2G425AF5J4TAM2.json","graph_json":"https://pith.science/api/pith-number/H3RH3TILIQBH2G425AF5J4TAM2/graph.json","events_json":"https://pith.science/api/pith-number/H3RH3TILIQBH2G425AF5J4TAM2/events.json","paper":"https://pith.science/paper/H3RH3TIL"},"agent_actions":{"view_html":"https://pith.science/pith/H3RH3TILIQBH2G425AF5J4TAM2","download_json":"https://pith.science/pith/H3RH3TILIQBH2G425AF5J4TAM2.json","view_paper":"https://pith.science/paper/H3RH3TIL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.24140&json=true","fetch_graph":"https://pith.science/api/pith-number/H3RH3TILIQBH2G425AF5J4TAM2/graph.json","fetch_events":"https://pith.science/api/pith-number/H3RH3TILIQBH2G425AF5J4TAM2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H3RH3TILIQBH2G425AF5J4TAM2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H3RH3TILIQBH2G425AF5J4TAM2/action/storage_attestation","attest_author":"https://pith.science/pith/H3RH3TILIQBH2G425AF5J4TAM2/action/author_attestation","sign_citation":"https://pith.science/pith/H3RH3TILIQBH2G425AF5J4TAM2/action/citation_signature","submit_replication":"https://pith.science/pith/H3RH3TILIQBH2G425AF5J4TAM2/action/replication_record"}},"created_at":"2026-06-24T01:14:42.081389+00:00","updated_at":"2026-06-24T01:14:42.081389+00:00"}