{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:VY5A7LIYUIVIO5TPR4THUKIE4S","short_pith_number":"pith:VY5A7LIY","canonical_record":{"source":{"id":"2412.04062","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-05T10:57:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3c58737ec084ff67e86bd7ab4db5334f666256e5da4c6e73df8be395bd7bc621","abstract_canon_sha256":"0309ad1a8fc30a709bb59e4e00c142d87574f06ed9d59eb4578b6cbe9dfff822"},"schema_version":"1.0"},"canonical_sha256":"ae3a0fad18a22a87766f8f267a2904e4a71a3b9d3b046b749c23e84bcdd8094e","source":{"kind":"arxiv","id":"2412.04062","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04062","created_at":"2026-07-05T11:29:04Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04062v3","created_at":"2026-07-05T11:29:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04062","created_at":"2026-07-05T11:29:04Z"},{"alias_kind":"pith_short_12","alias_value":"VY5A7LIYUIVI","created_at":"2026-07-05T11:29:04Z"},{"alias_kind":"pith_short_16","alias_value":"VY5A7LIYUIVIO5TP","created_at":"2026-07-05T11:29:04Z"},{"alias_kind":"pith_short_8","alias_value":"VY5A7LIY","created_at":"2026-07-05T11:29:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:VY5A7LIYUIVIO5TPR4THUKIE4S","target":"record","payload":{"canonical_record":{"source":{"id":"2412.04062","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-05T10:57:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3c58737ec084ff67e86bd7ab4db5334f666256e5da4c6e73df8be395bd7bc621","abstract_canon_sha256":"0309ad1a8fc30a709bb59e4e00c142d87574f06ed9d59eb4578b6cbe9dfff822"},"schema_version":"1.0"},"canonical_sha256":"ae3a0fad18a22a87766f8f267a2904e4a71a3b9d3b046b749c23e84bcdd8094e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:04.383067Z","signature_b64":"CDlr9mx1GQwUBGzG77aEUaPSvUECh022Dhv6yFJHiSSbYVGXQD8jucHcaCJ6TNaqBcK0adqlHi7MVgejTPLnBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae3a0fad18a22a87766f8f267a2904e4a71a3b9d3b046b749c23e84bcdd8094e","last_reissued_at":"2026-07-05T11:29:04.382526Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:04.382526Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.04062","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:29:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wjia0dso/mYqBFk1Wv+IIso5CHH42e4eq1dRfRz8JIa/Aq8ED0xH1UgvT04QP1vRrFjK1mVnjx8tRHlmRwjTDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T00:31:14.848033Z"},"content_sha256":"7c543adf8b001e072374705a484068d108f9cc42bb4d42011a58fcd289089a5b","schema_version":"1.0","event_id":"sha256:7c543adf8b001e072374705a484068d108f9cc42bb4d42011a58fcd289089a5b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:VY5A7LIYUIVIO5TPR4THUKIE4S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ZipAR: Parallel Auto-regressive Image Generation through Spatial Locality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bohan Zhuang, Feng Chen, Hong Zhou, Kaipeng Zhang, Shaoxuan He, Yefei He, Yuanyu He","submitted_at":"2024-12-05T10:57:08Z","abstract_excerpt":"In this paper, we propose ZipAR, a training-free, plug-and-play parallel decoding framework for accelerating auto-regressive (AR) visual generation. The motivation stems from the observation that images exhibit local structures, and spatially distant regions tend to have minimal interdependence. Given a partially decoded set of visual tokens, in addition to the original next-token prediction scheme in the row dimension, the tokens corresponding to spatially adjacent regions in the column dimension can be decoded in parallel, enabling the ``next-set prediction'' paradigm. By decoding multiple t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04062","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/2412.04062/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":"2026-07-05T11:29:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6PmRrUH60ly9tRf0lJIzGfiCTI5W/OCjuCnbnRT1HSkHEKKoH5ds9CzczyAchuHSCe2IJJlK/zZvHC4HSKkNDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T00:31:14.848404Z"},"content_sha256":"7710023bb7f3955223c81de0d7e59300910033ddeaee1f133f2bf1683a359756","schema_version":"1.0","event_id":"sha256:7710023bb7f3955223c81de0d7e59300910033ddeaee1f133f2bf1683a359756"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VY5A7LIYUIVIO5TPR4THUKIE4S/bundle.json","state_url":"https://pith.science/pith/VY5A7LIYUIVIO5TPR4THUKIE4S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VY5A7LIYUIVIO5TPR4THUKIE4S/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":"2026-08-05T00:31:14Z","links":{"resolver":"https://pith.science/pith/VY5A7LIYUIVIO5TPR4THUKIE4S","bundle":"https://pith.science/pith/VY5A7LIYUIVIO5TPR4THUKIE4S/bundle.json","state":"https://pith.science/pith/VY5A7LIYUIVIO5TPR4THUKIE4S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VY5A7LIYUIVIO5TPR4THUKIE4S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VY5A7LIYUIVIO5TPR4THUKIE4S","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":"0309ad1a8fc30a709bb59e4e00c142d87574f06ed9d59eb4578b6cbe9dfff822","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-05T10:57:08Z","title_canon_sha256":"3c58737ec084ff67e86bd7ab4db5334f666256e5da4c6e73df8be395bd7bc621"},"schema_version":"1.0","source":{"id":"2412.04062","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04062","created_at":"2026-07-05T11:29:04Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04062v3","created_at":"2026-07-05T11:29:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04062","created_at":"2026-07-05T11:29:04Z"},{"alias_kind":"pith_short_12","alias_value":"VY5A7LIYUIVI","created_at":"2026-07-05T11:29:04Z"},{"alias_kind":"pith_short_16","alias_value":"VY5A7LIYUIVIO5TP","created_at":"2026-07-05T11:29:04Z"},{"alias_kind":"pith_short_8","alias_value":"VY5A7LIY","created_at":"2026-07-05T11:29:04Z"}],"graph_snapshots":[{"event_id":"sha256:7710023bb7f3955223c81de0d7e59300910033ddeaee1f133f2bf1683a359756","target":"graph","created_at":"2026-07-05T11:29:04Z","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/2412.04062/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose ZipAR, a training-free, plug-and-play parallel decoding framework for accelerating auto-regressive (AR) visual generation. The motivation stems from the observation that images exhibit local structures, and spatially distant regions tend to have minimal interdependence. Given a partially decoded set of visual tokens, in addition to the original next-token prediction scheme in the row dimension, the tokens corresponding to spatially adjacent regions in the column dimension can be decoded in parallel, enabling the ``next-set prediction'' paradigm. By decoding multiple t","authors_text":"Bohan Zhuang, Feng Chen, Hong Zhou, Kaipeng Zhang, Shaoxuan He, Yefei He, Yuanyu He","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-05T10:57:08Z","title":"ZipAR: Parallel Auto-regressive Image Generation through Spatial Locality"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04062","kind":"arxiv","version":3},"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":"sha256:7c543adf8b001e072374705a484068d108f9cc42bb4d42011a58fcd289089a5b","target":"record","created_at":"2026-07-05T11:29:04Z","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":"0309ad1a8fc30a709bb59e4e00c142d87574f06ed9d59eb4578b6cbe9dfff822","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-05T10:57:08Z","title_canon_sha256":"3c58737ec084ff67e86bd7ab4db5334f666256e5da4c6e73df8be395bd7bc621"},"schema_version":"1.0","source":{"id":"2412.04062","kind":"arxiv","version":3}},"canonical_sha256":"ae3a0fad18a22a87766f8f267a2904e4a71a3b9d3b046b749c23e84bcdd8094e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ae3a0fad18a22a87766f8f267a2904e4a71a3b9d3b046b749c23e84bcdd8094e","first_computed_at":"2026-07-05T11:29:04.382526Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:29:04.382526Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CDlr9mx1GQwUBGzG77aEUaPSvUECh022Dhv6yFJHiSSbYVGXQD8jucHcaCJ6TNaqBcK0adqlHi7MVgejTPLnBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:29:04.383067Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.04062","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c543adf8b001e072374705a484068d108f9cc42bb4d42011a58fcd289089a5b","sha256:7710023bb7f3955223c81de0d7e59300910033ddeaee1f133f2bf1683a359756"],"state_sha256":"daaabed4e77657d1b3783c6a30f5e7619aad0332ff60a9838665aaf2afe1caeb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7un79uYcp9V3ldL4MVcc3IJbipqATana5c9SDr341DESR9FjS+9+f02GWftJ+y1mVU7yV5TlHfcqMsiegFLHAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T00:31:14.850846Z","bundle_sha256":"8a49413a4f82c34dc7436d75f4bf13ff988a3eb5573ec8a7c96a394359374336"}}