{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GRASWBDXILPBKXFSK2FWAK7MPH","short_pith_number":"pith:GRASWBDX","schema_version":"1.0","canonical_sha256":"34412b047742de155cb2568b602bec79fbc7a287d2c6cf9b9ab93a84644849b7","source":{"kind":"arxiv","id":"2312.09193","version":3},"attestation_state":"computed","paper":{"title":"Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Huizhuo Yuan, Junkai Zhang, Quanquan Gu, Yiwen Kou, Yongqian Li, Zixiang Chen","submitted_at":"2023-12-14T18:14:11Z","abstract_excerpt":"Discrete diffusion models have emerged as powerful tools for high-quality data generation. Despite their success in discrete spaces, such as text generation tasks, the acceleration of discrete diffusion models remains under-explored. In this paper, we propose discrete non-Markov diffusion models (DNDM), which naturally induce the predetermined transition time set. This enables a training-free sampling algorithm that significantly reduces the number of function evaluations (i.e., calls to the neural network), making the sampling process much faster. Furthermore, we study the transition from fin"},"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":"2312.09193","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-14T18:14:11Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"a761fb3b8811f7e7651862d78034620b628a8351b10f117085a22a741bc250bd","abstract_canon_sha256":"e6289793f7fca49be429b92f260aa5de940168c48811c8a79e22b678790a487c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:07.336953Z","signature_b64":"PBwu8mPpDa3BInSncEWzX8EaR+EBM2rhKdz1o6W6uki51rHN/Ucw6jRH49apqMHo2Wa4N4mOrZ3xXZY95FVeBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34412b047742de155cb2568b602bec79fbc7a287d2c6cf9b9ab93a84644849b7","last_reissued_at":"2026-07-05T09:45:07.336369Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:07.336369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Huizhuo Yuan, Junkai Zhang, Quanquan Gu, Yiwen Kou, Yongqian Li, Zixiang Chen","submitted_at":"2023-12-14T18:14:11Z","abstract_excerpt":"Discrete diffusion models have emerged as powerful tools for high-quality data generation. Despite their success in discrete spaces, such as text generation tasks, the acceleration of discrete diffusion models remains under-explored. In this paper, we propose discrete non-Markov diffusion models (DNDM), which naturally induce the predetermined transition time set. This enables a training-free sampling algorithm that significantly reduces the number of function evaluations (i.e., calls to the neural network), making the sampling process much faster. Furthermore, we study the transition from fin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.09193","kind":"arxiv","version":3},"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/2312.09193/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":"2312.09193","created_at":"2026-07-05T09:45:07.336434+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.09193v3","created_at":"2026-07-05T09:45:07.336434+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.09193","created_at":"2026-07-05T09:45:07.336434+00:00"},{"alias_kind":"pith_short_12","alias_value":"GRASWBDXILPB","created_at":"2026-07-05T09:45:07.336434+00:00"},{"alias_kind":"pith_short_16","alias_value":"GRASWBDXILPBKXFS","created_at":"2026-07-05T09:45:07.336434+00:00"},{"alias_kind":"pith_short_8","alias_value":"GRASWBDX","created_at":"2026-07-05T09:45:07.336434+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.19726","citing_title":"Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2406.03736","citing_title":"Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2505.16933","citing_title":"LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning","ref_index":107,"is_internal_anchor":false},{"citing_arxiv_id":"2505.22618","citing_title":"Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2502.09992","citing_title":"Large Language Diffusion Models","ref_index":68,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GRASWBDXILPBKXFSK2FWAK7MPH","json":"https://pith.science/pith/GRASWBDXILPBKXFSK2FWAK7MPH.json","graph_json":"https://pith.science/api/pith-number/GRASWBDXILPBKXFSK2FWAK7MPH/graph.json","events_json":"https://pith.science/api/pith-number/GRASWBDXILPBKXFSK2FWAK7MPH/events.json","paper":"https://pith.science/paper/GRASWBDX"},"agent_actions":{"view_html":"https://pith.science/pith/GRASWBDXILPBKXFSK2FWAK7MPH","download_json":"https://pith.science/pith/GRASWBDXILPBKXFSK2FWAK7MPH.json","view_paper":"https://pith.science/paper/GRASWBDX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.09193&json=true","fetch_graph":"https://pith.science/api/pith-number/GRASWBDXILPBKXFSK2FWAK7MPH/graph.json","fetch_events":"https://pith.science/api/pith-number/GRASWBDXILPBKXFSK2FWAK7MPH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GRASWBDXILPBKXFSK2FWAK7MPH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GRASWBDXILPBKXFSK2FWAK7MPH/action/storage_attestation","attest_author":"https://pith.science/pith/GRASWBDXILPBKXFSK2FWAK7MPH/action/author_attestation","sign_citation":"https://pith.science/pith/GRASWBDXILPBKXFSK2FWAK7MPH/action/citation_signature","submit_replication":"https://pith.science/pith/GRASWBDXILPBKXFSK2FWAK7MPH/action/replication_record"}},"created_at":"2026-07-05T09:45:07.336434+00:00","updated_at":"2026-07-05T09:45:07.336434+00:00"}