{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:SDGNTVAN7HQ2RQB634BV25OGUY","short_pith_number":"pith:SDGNTVAN","canonical_record":{"source":{"id":"2410.20280","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-26T21:12:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3caeefd940d41652d9dba2ed9eee257ee92ab3b6b0f8ea6500945c4be5665a94","abstract_canon_sha256":"86c448e6e0745d1041019be9f18793bcf350cada7fdf773a8290aacba9f84273"},"schema_version":"1.0"},"canonical_sha256":"90ccd9d40df9e1a8c03edf035d75c6a60937059bc4ab3722cde89da0c89d25bb","source":{"kind":"arxiv","id":"2410.20280","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.20280","created_at":"2026-07-05T09:26:42Z"},{"alias_kind":"arxiv_version","alias_value":"2410.20280v1","created_at":"2026-07-05T09:26:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.20280","created_at":"2026-07-05T09:26:42Z"},{"alias_kind":"pith_short_12","alias_value":"SDGNTVAN7HQ2","created_at":"2026-07-05T09:26:42Z"},{"alias_kind":"pith_short_16","alias_value":"SDGNTVAN7HQ2RQB6","created_at":"2026-07-05T09:26:42Z"},{"alias_kind":"pith_short_8","alias_value":"SDGNTVAN","created_at":"2026-07-05T09:26:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:SDGNTVAN7HQ2RQB634BV25OGUY","target":"record","payload":{"canonical_record":{"source":{"id":"2410.20280","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-26T21:12:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3caeefd940d41652d9dba2ed9eee257ee92ab3b6b0f8ea6500945c4be5665a94","abstract_canon_sha256":"86c448e6e0745d1041019be9f18793bcf350cada7fdf773a8290aacba9f84273"},"schema_version":"1.0"},"canonical_sha256":"90ccd9d40df9e1a8c03edf035d75c6a60937059bc4ab3722cde89da0c89d25bb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:26:42.019329Z","signature_b64":"LE/9W1kszVwtmjaQERmdy7vlXKbxlnxOCljsnYK/ZfihWyQxPuvWwIPajgEzYd4elg50JC57f6h9+ajSytjgBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90ccd9d40df9e1a8c03edf035d75c6a60937059bc4ab3722cde89da0c89d25bb","last_reissued_at":"2026-07-05T09:26:42.018827Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:26:42.018827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.20280","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":"2026-07-05T09:26:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5TqabQtcmt8dPtTGjpxQSss3/fC/VRi0QmP3mI8Us5WLxXJMM7wbcjJMlnUdc7ZQdKLd8QMy6FjneGe/Zo0CAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T13:27:04.546205Z"},"content_sha256":"8352a5abeb1b106cf98d8a7d69ee6012de4b9effe881387959f4f92e37b308de","schema_version":"1.0","event_id":"sha256:8352a5abeb1b106cf98d8a7d69ee6012de4b9effe881387959f4f92e37b308de"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:SDGNTVAN7HQ2RQB634BV25OGUY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MarDini: Masked Autoregressive Diffusion for Video Generation at Scale","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ding Liu, Haozhe Liu, Juan C. P\\'erez, Juan-Manuel P\\'erez-R\\'ua, Jui-Chieh Wu, J\\\"urgen Schmidhuber, Kumara Kahatapitiya, Menglin Jia, Mengmeng Xu, Sen He, Shikun Liu, Tao Xiang, Xiao Han, Yanping Xie, Zijian Zhou","submitted_at":"2024-10-26T21:12:32Z","abstract_excerpt":"We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework. Here, MAR handles temporal planning, while DM focuses on spatial generation in an asymmetric network design: i) a MAR-based planning model containing most of the parameters generates planning signals for each masked frame using low-resolution input; ii) a lightweight generation model uses these signals to produce high-resolution frames via diffusion de-noising. MarDini's MAR enables video generation conditioned on any number o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.20280","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/2410.20280/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":"2026-07-05T09:26:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MtphYfVeEcU3sWc0XukpvSGUnpvL/CIJkt29EeSd7gAgrYgmlL1qbsgyQ3qi8nDvRNL81Ls4X2ADarYNe/rxCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T13:27:04.546858Z"},"content_sha256":"2d648860702fadbd188a81c44b0595404178aa14e35f828787e7a8ff144e176e","schema_version":"1.0","event_id":"sha256:2d648860702fadbd188a81c44b0595404178aa14e35f828787e7a8ff144e176e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SDGNTVAN7HQ2RQB634BV25OGUY/bundle.json","state_url":"https://pith.science/pith/SDGNTVAN7HQ2RQB634BV25OGUY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SDGNTVAN7HQ2RQB634BV25OGUY/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":"2026-08-05T13:27:04Z","links":{"resolver":"https://pith.science/pith/SDGNTVAN7HQ2RQB634BV25OGUY","bundle":"https://pith.science/pith/SDGNTVAN7HQ2RQB634BV25OGUY/bundle.json","state":"https://pith.science/pith/SDGNTVAN7HQ2RQB634BV25OGUY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SDGNTVAN7HQ2RQB634BV25OGUY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SDGNTVAN7HQ2RQB634BV25OGUY","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":"86c448e6e0745d1041019be9f18793bcf350cada7fdf773a8290aacba9f84273","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-26T21:12:32Z","title_canon_sha256":"3caeefd940d41652d9dba2ed9eee257ee92ab3b6b0f8ea6500945c4be5665a94"},"schema_version":"1.0","source":{"id":"2410.20280","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.20280","created_at":"2026-07-05T09:26:42Z"},{"alias_kind":"arxiv_version","alias_value":"2410.20280v1","created_at":"2026-07-05T09:26:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.20280","created_at":"2026-07-05T09:26:42Z"},{"alias_kind":"pith_short_12","alias_value":"SDGNTVAN7HQ2","created_at":"2026-07-05T09:26:42Z"},{"alias_kind":"pith_short_16","alias_value":"SDGNTVAN7HQ2RQB6","created_at":"2026-07-05T09:26:42Z"},{"alias_kind":"pith_short_8","alias_value":"SDGNTVAN","created_at":"2026-07-05T09:26:42Z"}],"graph_snapshots":[{"event_id":"sha256:2d648860702fadbd188a81c44b0595404178aa14e35f828787e7a8ff144e176e","target":"graph","created_at":"2026-07-05T09:26:42Z","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/2410.20280/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework. Here, MAR handles temporal planning, while DM focuses on spatial generation in an asymmetric network design: i) a MAR-based planning model containing most of the parameters generates planning signals for each masked frame using low-resolution input; ii) a lightweight generation model uses these signals to produce high-resolution frames via diffusion de-noising. MarDini's MAR enables video generation conditioned on any number o","authors_text":"Ding Liu, Haozhe Liu, Juan C. P\\'erez, Juan-Manuel P\\'erez-R\\'ua, Jui-Chieh Wu, J\\\"urgen Schmidhuber, Kumara Kahatapitiya, Menglin Jia, Mengmeng Xu, Sen He, Shikun Liu, Tao Xiang, Xiao Han, Yanping Xie, Zijian Zhou","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-26T21:12:32Z","title":"MarDini: Masked Autoregressive Diffusion for Video Generation at Scale"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.20280","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":"sha256:8352a5abeb1b106cf98d8a7d69ee6012de4b9effe881387959f4f92e37b308de","target":"record","created_at":"2026-07-05T09:26:42Z","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":"86c448e6e0745d1041019be9f18793bcf350cada7fdf773a8290aacba9f84273","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-26T21:12:32Z","title_canon_sha256":"3caeefd940d41652d9dba2ed9eee257ee92ab3b6b0f8ea6500945c4be5665a94"},"schema_version":"1.0","source":{"id":"2410.20280","kind":"arxiv","version":1}},"canonical_sha256":"90ccd9d40df9e1a8c03edf035d75c6a60937059bc4ab3722cde89da0c89d25bb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"90ccd9d40df9e1a8c03edf035d75c6a60937059bc4ab3722cde89da0c89d25bb","first_computed_at":"2026-07-05T09:26:42.018827Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:26:42.018827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LE/9W1kszVwtmjaQERmdy7vlXKbxlnxOCljsnYK/ZfihWyQxPuvWwIPajgEzYd4elg50JC57f6h9+ajSytjgBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:26:42.019329Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.20280","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8352a5abeb1b106cf98d8a7d69ee6012de4b9effe881387959f4f92e37b308de","sha256:2d648860702fadbd188a81c44b0595404178aa14e35f828787e7a8ff144e176e"],"state_sha256":"bcd63b713996453d0aef1a1605f3302fb2714955974389e472011c4205a47a2e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iAv/JLIds3CMTWCDLYCv5nXnHfxxFuirmOm30AhGJOvBdoo5Peq1rAkjc+L8S+KIxb7v0WUhOII80iCFl14MAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T13:27:04.553244Z","bundle_sha256":"efc88c8394eefb296939c27f03f18c4b312f9edd3f0981a2fb85583b9cb049ac"}}