{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:3K6WDTWBAU2VBV7QVESGE5HRQB","short_pith_number":"pith:3K6WDTWB","canonical_record":{"source":{"id":"2607.00773","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T10:59:33Z","cross_cats_sorted":["cs.DC","cs.DS","cs.NA","math.NA"],"title_canon_sha256":"2679fec7d9ded4fecf9ff7633807f4e2fb82b5e669a7f06626df6614a9841986","abstract_canon_sha256":"f55bb40a94719ed8b5168c9bafa2612f45f9ab077a0bd4476b4fc84d234edb87"},"schema_version":"1.0"},"canonical_sha256":"dabd61cec1053550d7f0a9246274f1807655efcbade9dd779f38fc4ba46d53ca","source":{"kind":"arxiv","id":"2607.00773","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.00773","created_at":"2026-07-02T01:17:55Z"},{"alias_kind":"arxiv_version","alias_value":"2607.00773v1","created_at":"2026-07-02T01:17:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00773","created_at":"2026-07-02T01:17:55Z"},{"alias_kind":"pith_short_12","alias_value":"3K6WDTWBAU2V","created_at":"2026-07-02T01:17:55Z"},{"alias_kind":"pith_short_16","alias_value":"3K6WDTWBAU2VBV7Q","created_at":"2026-07-02T01:17:55Z"},{"alias_kind":"pith_short_8","alias_value":"3K6WDTWB","created_at":"2026-07-02T01:17:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:3K6WDTWBAU2VBV7QVESGE5HRQB","target":"record","payload":{"canonical_record":{"source":{"id":"2607.00773","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T10:59:33Z","cross_cats_sorted":["cs.DC","cs.DS","cs.NA","math.NA"],"title_canon_sha256":"2679fec7d9ded4fecf9ff7633807f4e2fb82b5e669a7f06626df6614a9841986","abstract_canon_sha256":"f55bb40a94719ed8b5168c9bafa2612f45f9ab077a0bd4476b4fc84d234edb87"},"schema_version":"1.0"},"canonical_sha256":"dabd61cec1053550d7f0a9246274f1807655efcbade9dd779f38fc4ba46d53ca","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:17:55.053592Z","signature_b64":"KvcnmKlH7NNHeBpsPSNBTfOU+CAV1hH6L+RhBu5KMTacVpJ69a7zi0sndOjnrC/JM04z/X0pBKkyfcKVUjhXDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dabd61cec1053550d7f0a9246274f1807655efcbade9dd779f38fc4ba46d53ca","last_reissued_at":"2026-07-02T01:17:55.053057Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:17:55.053057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.00773","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-02T01:17:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"grJtl20lIIafkZ1ruGVL/mksD5JB6+EVOKjY1u55qa9qkdHg4Y6d5SvGqPrftqiKbeUzFgTHDEgE9BgWozftAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T04:48:54.036544Z"},"content_sha256":"99a975621a9ba1be18297593860c738cbb92ba8db383d78a49b8c609274e5d4b","schema_version":"1.0","event_id":"sha256:99a975621a9ba1be18297593860c738cbb92ba8db383d78a49b8c609274e5d4b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:3K6WDTWBAU2VBV7QVESGE5HRQB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC","cs.DS","cs.NA","math.NA"],"primary_cat":"cs.LG","authors_text":"Andi Han, Huanjian Zhou, Masashi Sugiyama, Wei Huang, Yu Yao","submitted_at":"2026-07-01T10:59:33Z","abstract_excerpt":"Discrete diffusion models are widely used for learning and generating discrete distributions. As the generation process is inherently sequential, the acceleration of sampling is of significant importance. In this work, we parallelize the mainstream $\\tau$-leaping algorithm for absorbing discrete diffusion in a Continuous-Time Markov Chain (CTMC) framework. By leveraging the continuous-time stochastic integral form of the $\\tau$-leaping algorithm and the Picard iteration method, we achieve parallel-in-time sampling acceleration and provide a proof of exponential-factorial convergence for our al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00773","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/2607.00773/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-02T01:17:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bu2jHVyBFLCufmkiw2cZGHthHC45da9OB6IQaFhsIHVYtB3CdX1lpvsqywxDJrYijtPexFi6UxQPu2W9NpJNCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T04:48:54.036934Z"},"content_sha256":"ed2f4fac52dec6d8afb1c428b25004b20eb289d0f8a2a9817d6dc6324a64b9ca","schema_version":"1.0","event_id":"sha256:ed2f4fac52dec6d8afb1c428b25004b20eb289d0f8a2a9817d6dc6324a64b9ca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3K6WDTWBAU2VBV7QVESGE5HRQB/bundle.json","state_url":"https://pith.science/pith/3K6WDTWBAU2VBV7QVESGE5HRQB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3K6WDTWBAU2VBV7QVESGE5HRQB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-03T04:48:54Z","links":{"resolver":"https://pith.science/pith/3K6WDTWBAU2VBV7QVESGE5HRQB","bundle":"https://pith.science/pith/3K6WDTWBAU2VBV7QVESGE5HRQB/bundle.json","state":"https://pith.science/pith/3K6WDTWBAU2VBV7QVESGE5HRQB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3K6WDTWBAU2VBV7QVESGE5HRQB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:3K6WDTWBAU2VBV7QVESGE5HRQB","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":"f55bb40a94719ed8b5168c9bafa2612f45f9ab077a0bd4476b4fc84d234edb87","cross_cats_sorted":["cs.DC","cs.DS","cs.NA","math.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T10:59:33Z","title_canon_sha256":"2679fec7d9ded4fecf9ff7633807f4e2fb82b5e669a7f06626df6614a9841986"},"schema_version":"1.0","source":{"id":"2607.00773","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.00773","created_at":"2026-07-02T01:17:55Z"},{"alias_kind":"arxiv_version","alias_value":"2607.00773v1","created_at":"2026-07-02T01:17:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00773","created_at":"2026-07-02T01:17:55Z"},{"alias_kind":"pith_short_12","alias_value":"3K6WDTWBAU2V","created_at":"2026-07-02T01:17:55Z"},{"alias_kind":"pith_short_16","alias_value":"3K6WDTWBAU2VBV7Q","created_at":"2026-07-02T01:17:55Z"},{"alias_kind":"pith_short_8","alias_value":"3K6WDTWB","created_at":"2026-07-02T01:17:55Z"}],"graph_snapshots":[{"event_id":"sha256:ed2f4fac52dec6d8afb1c428b25004b20eb289d0f8a2a9817d6dc6324a64b9ca","target":"graph","created_at":"2026-07-02T01:17:55Z","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/2607.00773/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Discrete diffusion models are widely used for learning and generating discrete distributions. As the generation process is inherently sequential, the acceleration of sampling is of significant importance. In this work, we parallelize the mainstream $\\tau$-leaping algorithm for absorbing discrete diffusion in a Continuous-Time Markov Chain (CTMC) framework. By leveraging the continuous-time stochastic integral form of the $\\tau$-leaping algorithm and the Picard iteration method, we achieve parallel-in-time sampling acceleration and provide a proof of exponential-factorial convergence for our al","authors_text":"Andi Han, Huanjian Zhou, Masashi Sugiyama, Wei Huang, Yu Yao","cross_cats":["cs.DC","cs.DS","cs.NA","math.NA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T10:59:33Z","title":"Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00773","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":"sha256:99a975621a9ba1be18297593860c738cbb92ba8db383d78a49b8c609274e5d4b","target":"record","created_at":"2026-07-02T01:17:55Z","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":"f55bb40a94719ed8b5168c9bafa2612f45f9ab077a0bd4476b4fc84d234edb87","cross_cats_sorted":["cs.DC","cs.DS","cs.NA","math.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T10:59:33Z","title_canon_sha256":"2679fec7d9ded4fecf9ff7633807f4e2fb82b5e669a7f06626df6614a9841986"},"schema_version":"1.0","source":{"id":"2607.00773","kind":"arxiv","version":1}},"canonical_sha256":"dabd61cec1053550d7f0a9246274f1807655efcbade9dd779f38fc4ba46d53ca","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dabd61cec1053550d7f0a9246274f1807655efcbade9dd779f38fc4ba46d53ca","first_computed_at":"2026-07-02T01:17:55.053057Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-02T01:17:55.053057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KvcnmKlH7NNHeBpsPSNBTfOU+CAV1hH6L+RhBu5KMTacVpJ69a7zi0sndOjnrC/JM04z/X0pBKkyfcKVUjhXDQ==","signature_status":"signed_v1","signed_at":"2026-07-02T01:17:55.053592Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.00773","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:99a975621a9ba1be18297593860c738cbb92ba8db383d78a49b8c609274e5d4b","sha256:ed2f4fac52dec6d8afb1c428b25004b20eb289d0f8a2a9817d6dc6324a64b9ca"],"state_sha256":"98cabd30d9d670fbe09b4b4939333ec41d9e32fcd7fcbfd5c3d11212f39a61a9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ri1GaqmqrI3qiOeiUaCkri07pgDqCXcOmtzj9e/9F0Y2c7oBlDDAH13IuaJouWLXQyqj9M7iOdrNnxaHQq9ZAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T04:48:54.039020Z","bundle_sha256":"fa864a8c1a07710ab115a80ba8358e2ee4fcb442bf99d32a2bbaa08e3fa5fc89"}}