{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:PAAQFJO2ZD7BKQOQ3TSFRUDYJX","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":"a020834c64cf87be66864e2574d8e639050c17688de0ece23ddf2076769bb94c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-23T17:42:13Z","title_canon_sha256":"0a924cf7df5aaceac03efc5e53555ad0780e0f6f9f514993ae54c55993d28d8a"},"schema_version":"1.0","source":{"id":"2507.17801","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.17801","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"arxiv_version","alias_value":"2507.17801v1","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.17801","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"pith_short_12","alias_value":"PAAQFJO2ZD7B","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"pith_short_16","alias_value":"PAAQFJO2ZD7BKQOQ","created_at":"2026-07-05T11:42:35Z"},{"alias_kind":"pith_short_8","alias_value":"PAAQFJO2","created_at":"2026-07-05T11:42:35Z"}],"graph_snapshots":[{"event_id":"sha256:1cfbf053f70ce178fd78d7d5b01a081468544a6a2c7f884a7b3a93bb5a0a70c3","target":"graph","created_at":"2026-07-05T11:42:35Z","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/2507.17801/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present Lumina-mGPT 2.0, a stand-alone, decoder-only autoregressive model that revisits and revitalizes the autoregressive paradigm for high-quality image generation and beyond. Unlike existing approaches that rely on pretrained components or hybrid architectures, Lumina-mGPT 2.0 is trained entirely from scratch, enabling unrestricted architectural design and licensing freedom. It achieves generation quality on par with state-of-the-art diffusion models such as DALL-E 3 and SANA, while preserving the inherent flexibility and compositionality of autoregressive modeling. Our unified tokenizat","authors_text":"Bin Fu, Dongyang Liu, Guangtao Zhai, Hongsheng Li, Juncheng Yan, Le Zhuo, Mengmeng Wang, Peng Gao, Qi Qin, Renrui Zhang, Shicheng Li, Siqi Luo, Tiancheng Han, Victor Shea-Jay Huang, Xiaohong Liu, Xiaoqing Sun, Yi Xin, Yuewen Cao, Yupeng Zhou, Yu Qiao, Zhen Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-23T17:42:13Z","title":"Lumina-mGPT 2.0: Stand-Alone AutoRegressive Image Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.17801","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:d0f6b64db0057a310df24fc0aae1d4ac141a3cbf1da608063a17a9ece093ec93","target":"record","created_at":"2026-07-05T11:42:35Z","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":"a020834c64cf87be66864e2574d8e639050c17688de0ece23ddf2076769bb94c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-23T17:42:13Z","title_canon_sha256":"0a924cf7df5aaceac03efc5e53555ad0780e0f6f9f514993ae54c55993d28d8a"},"schema_version":"1.0","source":{"id":"2507.17801","kind":"arxiv","version":1}},"canonical_sha256":"780102a5dac8fe1541d0dce458d0784defd4fa6b632b80271ce3a198e675ee8b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"780102a5dac8fe1541d0dce458d0784defd4fa6b632b80271ce3a198e675ee8b","first_computed_at":"2026-07-05T11:42:35.547896Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:42:35.547896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jvzol1rhL3CNXzn++h0/MB0RiDK/rGcobliiCB8jNQxweXb/YOx2wrOLXbxM4T9XomVw5MV8fH+CHLMB1F4kCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:42:35.548402Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.17801","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d0f6b64db0057a310df24fc0aae1d4ac141a3cbf1da608063a17a9ece093ec93","sha256:1cfbf053f70ce178fd78d7d5b01a081468544a6a2c7f884a7b3a93bb5a0a70c3"],"state_sha256":"371cdc6908e4f3686c35c775c88c99bd97bd709bafeac3475889aea48d474611"}