{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TMKIP2WYLKEAUJJ7PNZIKRAICM","short_pith_number":"pith:TMKIP2WY","schema_version":"1.0","canonical_sha256":"9b1487ead85a880a253f7b7285440813093c00f09696167a45d1d21a53fe5078","source":{"kind":"arxiv","id":"2605.23346","version":1},"attestation_state":"computed","paper":{"title":"Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jaihoon Kim, Minhyuk Sung, Morteza Mardani, Prin Phunyaphibarn, Seungjun Kim, Taehoon Yoon","submitted_at":"2026-05-22T08:06:52Z","abstract_excerpt":"Discrete diffusion models have emerged as powerful frameworks for generating structured categorical data. However, efficiently sampling from reward-tilted distributions remains a fundamental challenge. While Twisted Sequential Monte Carlo (SMC) offers asymptotic exactness for this task, estimating the optimal twist function in discrete state spaces necessitates costly Monte Carlo approximations, resulting a severe computational bottleneck at inference. To overcome this limitation, we introduce Contrastive Distribution Matching (CDM), a novel framework that amortizes the cost of SMC inference b"},"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":"2605.23346","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T08:06:52Z","cross_cats_sorted":[],"title_canon_sha256":"4ce3b6ce10813d436d2aca75bdff56195ee477f10ca10207a1f389f08ba97b12","abstract_canon_sha256":"7a665e6d147abfe5ed626b2a84a56772d106de0f605b874f9055aa216d023faa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:50.136367Z","signature_b64":"p7uO70frr9lAjFhLKbsk6yIolwz4d2LDM8+pFr97vvC/mkFCr4ZKrygaJ0C/biCBu/9ksQtLB3Sj9mG9OYOCCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b1487ead85a880a253f7b7285440813093c00f09696167a45d1d21a53fe5078","last_reissued_at":"2026-05-25T02:01:50.135820Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:50.135820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jaihoon Kim, Minhyuk Sung, Morteza Mardani, Prin Phunyaphibarn, Seungjun Kim, Taehoon Yoon","submitted_at":"2026-05-22T08:06:52Z","abstract_excerpt":"Discrete diffusion models have emerged as powerful frameworks for generating structured categorical data. However, efficiently sampling from reward-tilted distributions remains a fundamental challenge. While Twisted Sequential Monte Carlo (SMC) offers asymptotic exactness for this task, estimating the optimal twist function in discrete state spaces necessitates costly Monte Carlo approximations, resulting a severe computational bottleneck at inference. To overcome this limitation, we introduce Contrastive Distribution Matching (CDM), a novel framework that amortizes the cost of SMC inference b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23346","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/2605.23346/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":"2605.23346","created_at":"2026-05-25T02:01:50.135906+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.23346v1","created_at":"2026-05-25T02:01:50.135906+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23346","created_at":"2026-05-25T02:01:50.135906+00:00"},{"alias_kind":"pith_short_12","alias_value":"TMKIP2WYLKEA","created_at":"2026-05-25T02:01:50.135906+00:00"},{"alias_kind":"pith_short_16","alias_value":"TMKIP2WYLKEAUJJ7","created_at":"2026-05-25T02:01:50.135906+00:00"},{"alias_kind":"pith_short_8","alias_value":"TMKIP2WY","created_at":"2026-05-25T02:01:50.135906+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/TMKIP2WYLKEAUJJ7PNZIKRAICM","json":"https://pith.science/pith/TMKIP2WYLKEAUJJ7PNZIKRAICM.json","graph_json":"https://pith.science/api/pith-number/TMKIP2WYLKEAUJJ7PNZIKRAICM/graph.json","events_json":"https://pith.science/api/pith-number/TMKIP2WYLKEAUJJ7PNZIKRAICM/events.json","paper":"https://pith.science/paper/TMKIP2WY"},"agent_actions":{"view_html":"https://pith.science/pith/TMKIP2WYLKEAUJJ7PNZIKRAICM","download_json":"https://pith.science/pith/TMKIP2WYLKEAUJJ7PNZIKRAICM.json","view_paper":"https://pith.science/paper/TMKIP2WY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.23346&json=true","fetch_graph":"https://pith.science/api/pith-number/TMKIP2WYLKEAUJJ7PNZIKRAICM/graph.json","fetch_events":"https://pith.science/api/pith-number/TMKIP2WYLKEAUJJ7PNZIKRAICM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TMKIP2WYLKEAUJJ7PNZIKRAICM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TMKIP2WYLKEAUJJ7PNZIKRAICM/action/storage_attestation","attest_author":"https://pith.science/pith/TMKIP2WYLKEAUJJ7PNZIKRAICM/action/author_attestation","sign_citation":"https://pith.science/pith/TMKIP2WYLKEAUJJ7PNZIKRAICM/action/citation_signature","submit_replication":"https://pith.science/pith/TMKIP2WYLKEAUJJ7PNZIKRAICM/action/replication_record"}},"created_at":"2026-05-25T02:01:50.135906+00:00","updated_at":"2026-05-25T02:01:50.135906+00:00"}