{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:AGHTHD3Z5S4TQPREBIG3IEIDUU","short_pith_number":"pith:AGHTHD3Z","canonical_record":{"source":{"id":"2501.16085","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-27T14:33:27Z","cross_cats_sorted":[],"title_canon_sha256":"a5de825ef729aed2f9f382da2bd8ad513f2222574bb9d9a953a6f4a083b206e4","abstract_canon_sha256":"495dee9bce52b925f6c4b4df476123f983753c402b5601255d8d46fff3bd302c"},"schema_version":"1.0"},"canonical_sha256":"018f338f79ecb9383e240a0db41103a520515f4ad0d463d74c766cd0cf32702b","source":{"kind":"arxiv","id":"2501.16085","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.16085","created_at":"2026-07-05T11:21:41Z"},{"alias_kind":"arxiv_version","alias_value":"2501.16085v2","created_at":"2026-07-05T11:21:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16085","created_at":"2026-07-05T11:21:41Z"},{"alias_kind":"pith_short_12","alias_value":"AGHTHD3Z5S4T","created_at":"2026-07-05T11:21:41Z"},{"alias_kind":"pith_short_16","alias_value":"AGHTHD3Z5S4TQPRE","created_at":"2026-07-05T11:21:41Z"},{"alias_kind":"pith_short_8","alias_value":"AGHTHD3Z","created_at":"2026-07-05T11:21:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:AGHTHD3Z5S4TQPREBIG3IEIDUU","target":"record","payload":{"canonical_record":{"source":{"id":"2501.16085","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-27T14:33:27Z","cross_cats_sorted":[],"title_canon_sha256":"a5de825ef729aed2f9f382da2bd8ad513f2222574bb9d9a953a6f4a083b206e4","abstract_canon_sha256":"495dee9bce52b925f6c4b4df476123f983753c402b5601255d8d46fff3bd302c"},"schema_version":"1.0"},"canonical_sha256":"018f338f79ecb9383e240a0db41103a520515f4ad0d463d74c766cd0cf32702b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:41.122469Z","signature_b64":"0hTcaZqengu6WiwfXBQlcc/KbWeRm14eiNYIOu60hZfU81/AQuqbFSbNAU5bfzZ4eRsZjDF6XWUBv3rJFgQcCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"018f338f79ecb9383e240a0db41103a520515f4ad0d463d74c766cd0cf32702b","last_reissued_at":"2026-07-05T11:21:41.121967Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:41.121967Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.16085","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:21:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kNhjyAJR1lcWuFpIJWiVhpaeRl9Z3+/ZGKzNuNG7T4+MgpfqD8WqsCoSsiEXmFbtTjdUB15heVU36FmeliC7AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T14:12:55.686320Z"},"content_sha256":"7bd5ca494e24e3a87f1500f49f57356c299f1999fe13cd9e27e063aa5fa0d92a","schema_version":"1.0","event_id":"sha256:7bd5ca494e24e3a87f1500f49f57356c299f1999fe13cd9e27e063aa5fa0d92a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:AGHTHD3Z5S4TQPREBIG3IEIDUU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ARFlow: Autoregressive Flow with Hybrid Linear Attention","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cihang Xie, Jason Eshraghian, Mude Hui, Rui-Jie Zhu, Songlin Yang, Yuyin Zhou, Yu Zhang, Zirui Wang","submitted_at":"2025-01-27T14:33:27Z","abstract_excerpt":"Flow models are effective at progressively generating realistic images, but they generally struggle to capture long-range dependencies during the generation process as they compress all the information from previous time steps into a single corrupted image. To address this limitation, we propose integrating autoregressive modeling -- known for its excellence in modeling complex, high-dimensional joint probability distributions -- into flow models. During training, at each step, we construct causally-ordered sequences by sampling multiple images from the same semantic category and applying diff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16085","kind":"arxiv","version":2},"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/2501.16085/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":"2026-07-05T11:21:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WHpYhYtd8SMnx0DwnW0jP7BHwI6pDGjNYVOUTN8TmK/SoP7I1LC67Uku4hQlqJUn3V9R9frZesDZxat5Rc1+Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T14:12:55.686931Z"},"content_sha256":"9c0c2d30bbdf03bb890c7541817ead99f5f7229de8add6026fad6275f111f7bd","schema_version":"1.0","event_id":"sha256:9c0c2d30bbdf03bb890c7541817ead99f5f7229de8add6026fad6275f111f7bd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AGHTHD3Z5S4TQPREBIG3IEIDUU/bundle.json","state_url":"https://pith.science/pith/AGHTHD3Z5S4TQPREBIG3IEIDUU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AGHTHD3Z5S4TQPREBIG3IEIDUU/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":"2026-08-06T14:12:55Z","links":{"resolver":"https://pith.science/pith/AGHTHD3Z5S4TQPREBIG3IEIDUU","bundle":"https://pith.science/pith/AGHTHD3Z5S4TQPREBIG3IEIDUU/bundle.json","state":"https://pith.science/pith/AGHTHD3Z5S4TQPREBIG3IEIDUU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AGHTHD3Z5S4TQPREBIG3IEIDUU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:AGHTHD3Z5S4TQPREBIG3IEIDUU","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":"495dee9bce52b925f6c4b4df476123f983753c402b5601255d8d46fff3bd302c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-27T14:33:27Z","title_canon_sha256":"a5de825ef729aed2f9f382da2bd8ad513f2222574bb9d9a953a6f4a083b206e4"},"schema_version":"1.0","source":{"id":"2501.16085","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.16085","created_at":"2026-07-05T11:21:41Z"},{"alias_kind":"arxiv_version","alias_value":"2501.16085v2","created_at":"2026-07-05T11:21:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16085","created_at":"2026-07-05T11:21:41Z"},{"alias_kind":"pith_short_12","alias_value":"AGHTHD3Z5S4T","created_at":"2026-07-05T11:21:41Z"},{"alias_kind":"pith_short_16","alias_value":"AGHTHD3Z5S4TQPRE","created_at":"2026-07-05T11:21:41Z"},{"alias_kind":"pith_short_8","alias_value":"AGHTHD3Z","created_at":"2026-07-05T11:21:41Z"}],"graph_snapshots":[{"event_id":"sha256:9c0c2d30bbdf03bb890c7541817ead99f5f7229de8add6026fad6275f111f7bd","target":"graph","created_at":"2026-07-05T11:21:41Z","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/2501.16085/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Flow models are effective at progressively generating realistic images, but they generally struggle to capture long-range dependencies during the generation process as they compress all the information from previous time steps into a single corrupted image. To address this limitation, we propose integrating autoregressive modeling -- known for its excellence in modeling complex, high-dimensional joint probability distributions -- into flow models. During training, at each step, we construct causally-ordered sequences by sampling multiple images from the same semantic category and applying diff","authors_text":"Cihang Xie, Jason Eshraghian, Mude Hui, Rui-Jie Zhu, Songlin Yang, Yuyin Zhou, Yu Zhang, Zirui Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-27T14:33:27Z","title":"ARFlow: Autoregressive Flow with Hybrid Linear Attention"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16085","kind":"arxiv","version":2},"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":"sha256:7bd5ca494e24e3a87f1500f49f57356c299f1999fe13cd9e27e063aa5fa0d92a","target":"record","created_at":"2026-07-05T11:21:41Z","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":"495dee9bce52b925f6c4b4df476123f983753c402b5601255d8d46fff3bd302c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-27T14:33:27Z","title_canon_sha256":"a5de825ef729aed2f9f382da2bd8ad513f2222574bb9d9a953a6f4a083b206e4"},"schema_version":"1.0","source":{"id":"2501.16085","kind":"arxiv","version":2}},"canonical_sha256":"018f338f79ecb9383e240a0db41103a520515f4ad0d463d74c766cd0cf32702b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"018f338f79ecb9383e240a0db41103a520515f4ad0d463d74c766cd0cf32702b","first_computed_at":"2026-07-05T11:21:41.121967Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:41.121967Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0hTcaZqengu6WiwfXBQlcc/KbWeRm14eiNYIOu60hZfU81/AQuqbFSbNAU5bfzZ4eRsZjDF6XWUBv3rJFgQcCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:41.122469Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.16085","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7bd5ca494e24e3a87f1500f49f57356c299f1999fe13cd9e27e063aa5fa0d92a","sha256:9c0c2d30bbdf03bb890c7541817ead99f5f7229de8add6026fad6275f111f7bd"],"state_sha256":"ac4cdb5372b7df072f79c59e7371fb67e7cfbb59886665b7337bd79481dbb9f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NIYgjwcmshYDIU02LYFgBIuAaS6WfHZCVAlusV57kSCPFa1X0XsOKOe/nCnwUoVJO3G4HhwqIoXt18p95GYcAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T14:12:55.691205Z","bundle_sha256":"1a7687b7a84bce86decfd85fa65861d89627d61e17e4426b315adb47f4aa8188"}}