{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:L62XB75GLZOH57GXECXGI26CBN","short_pith_number":"pith:L62XB75G","canonical_record":{"source":{"id":"2412.15205","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-19T18:59:31Z","cross_cats_sorted":[],"title_canon_sha256":"32e4eccf89a2e600629a9219273084794c6c58e14aeedd76944a7eaa6df672c8","abstract_canon_sha256":"7a5e786cd05aa71aa9044bd1e6538f2444f5aa5167e3c16a596bf51f56e4f7be"},"schema_version":"1.0"},"canonical_sha256":"5fb570ffa65e5c7efcd720ae646bc20b4b55a1249524432584b1e2b0af5e1da3","source":{"kind":"arxiv","id":"2412.15205","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.15205","created_at":"2026-07-05T09:51:59Z"},{"alias_kind":"arxiv_version","alias_value":"2412.15205v1","created_at":"2026-07-05T09:51:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.15205","created_at":"2026-07-05T09:51:59Z"},{"alias_kind":"pith_short_12","alias_value":"L62XB75GLZOH","created_at":"2026-07-05T09:51:59Z"},{"alias_kind":"pith_short_16","alias_value":"L62XB75GLZOH57GX","created_at":"2026-07-05T09:51:59Z"},{"alias_kind":"pith_short_8","alias_value":"L62XB75G","created_at":"2026-07-05T09:51:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:L62XB75GLZOH57GXECXGI26CBN","target":"record","payload":{"canonical_record":{"source":{"id":"2412.15205","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-19T18:59:31Z","cross_cats_sorted":[],"title_canon_sha256":"32e4eccf89a2e600629a9219273084794c6c58e14aeedd76944a7eaa6df672c8","abstract_canon_sha256":"7a5e786cd05aa71aa9044bd1e6538f2444f5aa5167e3c16a596bf51f56e4f7be"},"schema_version":"1.0"},"canonical_sha256":"5fb570ffa65e5c7efcd720ae646bc20b4b55a1249524432584b1e2b0af5e1da3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:59.182691Z","signature_b64":"8YIdjWa0+PEa5mnZFYWqyx+W1AkD6Q8+PkZFAkFfvTwlixek2rY3yRYXcyNVH8iGsHTEt+wm/eihdmyiIIRVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fb570ffa65e5c7efcd720ae646bc20b4b55a1249524432584b1e2b0af5e1da3","last_reissued_at":"2026-07-05T09:51:59.182216Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:59.182216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.15205","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":"2026-07-05T09:51:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YOF7wfdTFUJKLCNcsPpdRwkYLTbAHCWezv4kBGGWaDJVZMGtwa/BNY50ERyYI1fcDM6f3tFPI/88YVzrBUUoCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T12:23:21.493610Z"},"content_sha256":"a791b31e2c9094ce5fbd1b854b515928fb4a562e8dac35916b179629b4aa02f6","schema_version":"1.0","event_id":"sha256:a791b31e2c9094ce5fbd1b854b515928fb4a562e8dac35916b179629b4aa02f6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:L62XB75GLZOH57GXECXGI26CBN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan Yuille, Ju He, Liang-Chieh Chen, Qihang Yu, Sucheng Ren, Xiaohui Shen","submitted_at":"2024-12-19T18:59:31Z","abstract_excerpt":"Autoregressive (AR) modeling has achieved remarkable success in natural language processing by enabling models to generate text with coherence and contextual understanding through next token prediction. Recently, in image generation, VAR proposes scale-wise autoregressive modeling, which extends the next token prediction to the next scale prediction, preserving the 2D structure of images. However, VAR encounters two primary challenges: (1) its complex and rigid scale design limits generalization in next scale prediction, and (2) the generator's dependence on a discrete tokenizer with the same "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.15205","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/2412.15205/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":"2026-07-05T09:51:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WdpRFJDyJnZO57xEsUaZ3Ibx8AGSeHTdcAfgE853hLc3xuqly8ae3AzC6EAofhj39cbWsYesSuRGjLwP/FPVDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T12:23:21.493982Z"},"content_sha256":"3acc5eee47f6dbd4dd96778849bc006484c6e973aef6f1ffb51a1c236d3cf564","schema_version":"1.0","event_id":"sha256:3acc5eee47f6dbd4dd96778849bc006484c6e973aef6f1ffb51a1c236d3cf564"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L62XB75GLZOH57GXECXGI26CBN/bundle.json","state_url":"https://pith.science/pith/L62XB75GLZOH57GXECXGI26CBN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L62XB75GLZOH57GXECXGI26CBN/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":"2026-07-07T12:23:21Z","links":{"resolver":"https://pith.science/pith/L62XB75GLZOH57GXECXGI26CBN","bundle":"https://pith.science/pith/L62XB75GLZOH57GXECXGI26CBN/bundle.json","state":"https://pith.science/pith/L62XB75GLZOH57GXECXGI26CBN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L62XB75GLZOH57GXECXGI26CBN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:L62XB75GLZOH57GXECXGI26CBN","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":"7a5e786cd05aa71aa9044bd1e6538f2444f5aa5167e3c16a596bf51f56e4f7be","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-19T18:59:31Z","title_canon_sha256":"32e4eccf89a2e600629a9219273084794c6c58e14aeedd76944a7eaa6df672c8"},"schema_version":"1.0","source":{"id":"2412.15205","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.15205","created_at":"2026-07-05T09:51:59Z"},{"alias_kind":"arxiv_version","alias_value":"2412.15205v1","created_at":"2026-07-05T09:51:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.15205","created_at":"2026-07-05T09:51:59Z"},{"alias_kind":"pith_short_12","alias_value":"L62XB75GLZOH","created_at":"2026-07-05T09:51:59Z"},{"alias_kind":"pith_short_16","alias_value":"L62XB75GLZOH57GX","created_at":"2026-07-05T09:51:59Z"},{"alias_kind":"pith_short_8","alias_value":"L62XB75G","created_at":"2026-07-05T09:51:59Z"}],"graph_snapshots":[{"event_id":"sha256:3acc5eee47f6dbd4dd96778849bc006484c6e973aef6f1ffb51a1c236d3cf564","target":"graph","created_at":"2026-07-05T09:51:59Z","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/2412.15205/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Autoregressive (AR) modeling has achieved remarkable success in natural language processing by enabling models to generate text with coherence and contextual understanding through next token prediction. Recently, in image generation, VAR proposes scale-wise autoregressive modeling, which extends the next token prediction to the next scale prediction, preserving the 2D structure of images. However, VAR encounters two primary challenges: (1) its complex and rigid scale design limits generalization in next scale prediction, and (2) the generator's dependence on a discrete tokenizer with the same ","authors_text":"Alan Yuille, Ju He, Liang-Chieh Chen, Qihang Yu, Sucheng Ren, Xiaohui Shen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-19T18:59:31Z","title":"FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.15205","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:a791b31e2c9094ce5fbd1b854b515928fb4a562e8dac35916b179629b4aa02f6","target":"record","created_at":"2026-07-05T09:51:59Z","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":"7a5e786cd05aa71aa9044bd1e6538f2444f5aa5167e3c16a596bf51f56e4f7be","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-19T18:59:31Z","title_canon_sha256":"32e4eccf89a2e600629a9219273084794c6c58e14aeedd76944a7eaa6df672c8"},"schema_version":"1.0","source":{"id":"2412.15205","kind":"arxiv","version":1}},"canonical_sha256":"5fb570ffa65e5c7efcd720ae646bc20b4b55a1249524432584b1e2b0af5e1da3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5fb570ffa65e5c7efcd720ae646bc20b4b55a1249524432584b1e2b0af5e1da3","first_computed_at":"2026-07-05T09:51:59.182216Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:51:59.182216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8YIdjWa0+PEa5mnZFYWqyx+W1AkD6Q8+PkZFAkFfvTwlixek2rY3yRYXcyNVH8iGsHTEt+wm/eihdmyiIIRVDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:51:59.182691Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.15205","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a791b31e2c9094ce5fbd1b854b515928fb4a562e8dac35916b179629b4aa02f6","sha256:3acc5eee47f6dbd4dd96778849bc006484c6e973aef6f1ffb51a1c236d3cf564"],"state_sha256":"cbf545a42755c21f441c865ecac693b0fc18fef88b904e03d88eefe400258b9e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cBlB0KlNhn6hQAFtgeaYuU0Hjj2tUYpjXZ0QqK5BdBQTs4bRWS3o70xdAScybm+adsdJb0SlE/pcd6uM4LjODQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T12:23:21.495934Z","bundle_sha256":"c30358a31a74fcaa278381b4e733df2bbe8acd3e4d4219db04deacf90bedeb36"}}