{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KOK7SOFPKBBA7ZSJMY6BHM6BUB","short_pith_number":"pith:KOK7SOFP","canonical_record":{"source":{"id":"2506.10962","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-12T17:57:44Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"77aa57189f0407e50193751ea9e6e76aedfc8aedf4a62371927d62ee71f5e45c","abstract_canon_sha256":"4ab834f6244ac9501bbf5709978a2ab8f4d2f259fc1f0ba39993633d0fa2e3a9"},"schema_version":"1.0"},"canonical_sha256":"5395f938af50420fe649663c13b3c1a05a7c9258e8a508de452db3971f78edc3","source":{"kind":"arxiv","id":"2506.10962","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.10962","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"arxiv_version","alias_value":"2506.10962v1","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.10962","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"pith_short_12","alias_value":"KOK7SOFPKBBA","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"pith_short_16","alias_value":"KOK7SOFPKBBA7ZSJ","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"pith_short_8","alias_value":"KOK7SOFP","created_at":"2026-07-05T11:20:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KOK7SOFPKBBA7ZSJMY6BHM6BUB","target":"record","payload":{"canonical_record":{"source":{"id":"2506.10962","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-12T17:57:44Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"77aa57189f0407e50193751ea9e6e76aedfc8aedf4a62371927d62ee71f5e45c","abstract_canon_sha256":"4ab834f6244ac9501bbf5709978a2ab8f4d2f259fc1f0ba39993633d0fa2e3a9"},"schema_version":"1.0"},"canonical_sha256":"5395f938af50420fe649663c13b3c1a05a7c9258e8a508de452db3971f78edc3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:20:41.171211Z","signature_b64":"6x+R/XyIhYKXlv5azX/XS/keU9hirScm/T88GwG7oX2+hNMn7W/IrQ8pE11jxpWVbvYUhwld1e/D4c998MRdAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5395f938af50420fe649663c13b3c1a05a7c9258e8a508de452db3971f78edc3","last_reissued_at":"2026-07-05T11:20:41.170761Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:20:41.170761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.10962","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":"2026-07-05T11:20:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F7VbK/+jz2Mrchy229TChnKxWPHgSuCwbGg0+cKhQe/FHUvboz/97ZJrC7s9EwRe64pY/4r9TVVwWgI35Wy2CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:13:13.388665Z"},"content_sha256":"d68986e2173956f37386ee71c6a654995636064f44e56bebe9304cb179f2b68f","schema_version":"1.0","event_id":"sha256:d68986e2173956f37386ee71c6a654995636064f44e56bebe9304cb179f2b68f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KOK7SOFPKBBA7ZSJMY6BHM6BUB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SpectralAR: Spectral Autoregressive Visual Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jie Zhou, Jiwen Lu, Weiliang Chen, Wenzhao Zheng, Yuanhui Huang, Yueqi Duan","submitted_at":"2025-06-12T17:57:44Z","abstract_excerpt":"Autoregressive visual generation has garnered increasing attention due to its scalability and compatibility with other modalities compared with diffusion models. Most existing methods construct visual sequences as spatial patches for autoregressive generation. However, image patches are inherently parallel, contradicting the causal nature of autoregressive modeling. To address this, we propose a Spectral AutoRegressive (SpectralAR) visual generation framework, which realizes causality for visual sequences from the spectral perspective. Specifically, we first transform an image into ordered spe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.10962","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/2506.10962/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":"2026-07-05T11:20:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hzcCMkP3WQbiSx3Ei7Mo9fJY/iisdqfz+EYQ8xkyWWmg7MMWySV4Cn/2XheCMVcvi40ie5lNBHc8MXLMN8mtDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:13:13.389186Z"},"content_sha256":"edd2704dea9395ca0288a69838c6995cccae3027dd3018ab8cbbfd918f0def00","schema_version":"1.0","event_id":"sha256:edd2704dea9395ca0288a69838c6995cccae3027dd3018ab8cbbfd918f0def00"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KOK7SOFPKBBA7ZSJMY6BHM6BUB/bundle.json","state_url":"https://pith.science/pith/KOK7SOFPKBBA7ZSJMY6BHM6BUB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KOK7SOFPKBBA7ZSJMY6BHM6BUB/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":"2026-08-05T23:13:13Z","links":{"resolver":"https://pith.science/pith/KOK7SOFPKBBA7ZSJMY6BHM6BUB","bundle":"https://pith.science/pith/KOK7SOFPKBBA7ZSJMY6BHM6BUB/bundle.json","state":"https://pith.science/pith/KOK7SOFPKBBA7ZSJMY6BHM6BUB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KOK7SOFPKBBA7ZSJMY6BHM6BUB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KOK7SOFPKBBA7ZSJMY6BHM6BUB","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":"4ab834f6244ac9501bbf5709978a2ab8f4d2f259fc1f0ba39993633d0fa2e3a9","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-12T17:57:44Z","title_canon_sha256":"77aa57189f0407e50193751ea9e6e76aedfc8aedf4a62371927d62ee71f5e45c"},"schema_version":"1.0","source":{"id":"2506.10962","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.10962","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"arxiv_version","alias_value":"2506.10962v1","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.10962","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"pith_short_12","alias_value":"KOK7SOFPKBBA","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"pith_short_16","alias_value":"KOK7SOFPKBBA7ZSJ","created_at":"2026-07-05T11:20:41Z"},{"alias_kind":"pith_short_8","alias_value":"KOK7SOFP","created_at":"2026-07-05T11:20:41Z"}],"graph_snapshots":[{"event_id":"sha256:edd2704dea9395ca0288a69838c6995cccae3027dd3018ab8cbbfd918f0def00","target":"graph","created_at":"2026-07-05T11:20:41Z","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/2506.10962/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Autoregressive visual generation has garnered increasing attention due to its scalability and compatibility with other modalities compared with diffusion models. Most existing methods construct visual sequences as spatial patches for autoregressive generation. However, image patches are inherently parallel, contradicting the causal nature of autoregressive modeling. To address this, we propose a Spectral AutoRegressive (SpectralAR) visual generation framework, which realizes causality for visual sequences from the spectral perspective. Specifically, we first transform an image into ordered spe","authors_text":"Jie Zhou, Jiwen Lu, Weiliang Chen, Wenzhao Zheng, Yuanhui Huang, Yueqi Duan","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-12T17:57:44Z","title":"SpectralAR: Spectral Autoregressive Visual Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.10962","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:d68986e2173956f37386ee71c6a654995636064f44e56bebe9304cb179f2b68f","target":"record","created_at":"2026-07-05T11:20:41Z","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":"4ab834f6244ac9501bbf5709978a2ab8f4d2f259fc1f0ba39993633d0fa2e3a9","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-12T17:57:44Z","title_canon_sha256":"77aa57189f0407e50193751ea9e6e76aedfc8aedf4a62371927d62ee71f5e45c"},"schema_version":"1.0","source":{"id":"2506.10962","kind":"arxiv","version":1}},"canonical_sha256":"5395f938af50420fe649663c13b3c1a05a7c9258e8a508de452db3971f78edc3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5395f938af50420fe649663c13b3c1a05a7c9258e8a508de452db3971f78edc3","first_computed_at":"2026-07-05T11:20:41.170761Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:20:41.170761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6x+R/XyIhYKXlv5azX/XS/keU9hirScm/T88GwG7oX2+hNMn7W/IrQ8pE11jxpWVbvYUhwld1e/D4c998MRdAw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:20:41.171211Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.10962","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d68986e2173956f37386ee71c6a654995636064f44e56bebe9304cb179f2b68f","sha256:edd2704dea9395ca0288a69838c6995cccae3027dd3018ab8cbbfd918f0def00"],"state_sha256":"aa835cf60544d51770c05f5919a74688307aa097653394c01acbf05a1da601fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J4F2N+w5NlIOkzuVcySaCZXcL/fI5etfnuiXBEGYBAZkyEfTOMobl8JXURv7IKHEHYY+F5MFYJ4DpdTva+/FCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T23:13:13.393659Z","bundle_sha256":"90d0efd3c813648663ea8b05d76b896a41a20a2b46d3e2b7d104f35ff3b42849"}}