{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:SRUVOHSACTSVW3XXJKCF3EGTVQ","short_pith_number":"pith:SRUVOHSA","canonical_record":{"source":{"id":"2505.07818","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-12T17:59:34Z","cross_cats_sorted":[],"title_canon_sha256":"255a340e8bdede7b1072df20a0c8c822c889e1695410ecaaf935616224fa9107","abstract_canon_sha256":"56ad9173326fe9e6b18f0f05c6688f71c4f2d208b925a5dfba11e2965abea065"},"schema_version":"1.0"},"canonical_sha256":"9469571e4014e55b6ef74a845d90d3ac2c4a89e3edbc4f2264d840edb63cfaf6","source":{"kind":"arxiv","id":"2505.07818","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.07818","created_at":"2026-07-05T12:00:49Z"},{"alias_kind":"arxiv_version","alias_value":"2505.07818v4","created_at":"2026-07-05T12:00:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.07818","created_at":"2026-07-05T12:00:49Z"},{"alias_kind":"pith_short_12","alias_value":"SRUVOHSACTSV","created_at":"2026-07-05T12:00:49Z"},{"alias_kind":"pith_short_16","alias_value":"SRUVOHSACTSVW3XX","created_at":"2026-07-05T12:00:49Z"},{"alias_kind":"pith_short_8","alias_value":"SRUVOHSA","created_at":"2026-07-05T12:00:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:SRUVOHSACTSVW3XXJKCF3EGTVQ","target":"record","payload":{"canonical_record":{"source":{"id":"2505.07818","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-12T17:59:34Z","cross_cats_sorted":[],"title_canon_sha256":"255a340e8bdede7b1072df20a0c8c822c889e1695410ecaaf935616224fa9107","abstract_canon_sha256":"56ad9173326fe9e6b18f0f05c6688f71c4f2d208b925a5dfba11e2965abea065"},"schema_version":"1.0"},"canonical_sha256":"9469571e4014e55b6ef74a845d90d3ac2c4a89e3edbc4f2264d840edb63cfaf6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:49.962406Z","signature_b64":"PCi3x8Pspnau8xOR+B4jwjqM8npca/CTQdsk6iirs7oebBwWw+ewh+oT3VC4N1YGAq+tQI4ef0GL871wRpNZAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9469571e4014e55b6ef74a845d90d3ac2c4a89e3edbc4f2264d840edb63cfaf6","last_reissued_at":"2026-07-05T12:00:49.961801Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:49.961801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.07818","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:00:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PTrlgbOEfgEpCgi7F6GE4a/+EC/tlisCwj2/pHny77sFhzGDWtVLZ2ryo98pyhZOmhCFnxcT6vRseFZGpC0LBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:43:34.889637Z"},"content_sha256":"285d641d579e2188dd0aefbdf83d110f09f36026b83f17e8be13dcd9b2be1fd1","schema_version":"1.0","event_id":"sha256:285d641d579e2188dd0aefbdf83d110f09f36026b83f17e8be13dcd9b2be1fd1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:SRUVOHSACTSVW3XXJKCF3EGTVQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DanceGRPO: Unleashing GRPO on Visual Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"DanceGRPO adapts group relative policy optimization to stabilize reinforcement learning for image and video generation.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fangyuan Kong, Jie Wu, Lingting Zhu, Mengzhao Chen, Ping Luo, Qiushan Guo, Weilin Huang, Wei Liu, Yu Gao, Zeyue Xue, Zhiheng Liu","submitted_at":"2025-05-12T17:59:34Z","abstract_excerpt":"Recent advances in generative AI have revolutionized visual content creation, yet aligning model outputs with human preferences remains a critical challenge. While Reinforcement Learning (RL) has emerged as a promising approach for fine-tuning generative models, existing methods like DDPO and DPOK face fundamental limitations - particularly their inability to maintain stable optimization when scaling to large and diverse prompt sets, severely restricting their practical utility. This paper presents DanceGRPO, a framework that addresses these limitations through an innovative adaptation of Grou"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"DanceGRPO demonstrates consistent and stable policy optimization across multiple modern generative paradigms, including both diffusion models and rectified flows, maintains robust performance when scaling to complex real-world scenarios, and outperforms baseline methods by up to 181% across benchmarks including HPS-v2.1, CLIP Score, VideoAlign, and GenEval.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That GRPO's inherent stability mechanisms can be directly adapted to overcome the optimization instabilities of prior RL methods (DDPO, DPOK) when scaling to large and diverse prompt sets in visual generation without introducing new failure modes.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DanceGRPO applies GRPO to visual generation tasks to achieve stable policy optimization across diffusion models, rectified flows, multiple tasks, and diverse reward models, outperforming prior RL methods.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"DanceGRPO adapts group relative policy optimization to stabilize reinforcement learning for image and video generation.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4398021bb665815af6bd7092e3cf784db07e3b369343db00bd12d864c4e4c068"},"source":{"id":"2505.07818","kind":"arxiv","version":4},"verdict":{"id":"f4a2c9e8-14c3-41a3-af74-d3b098f7a7fa","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T22:23:41.241673Z","strongest_claim":"DanceGRPO demonstrates consistent and stable policy optimization across multiple modern generative paradigms, including both diffusion models and rectified flows, maintains robust performance when scaling to complex real-world scenarios, and outperforms baseline methods by up to 181% across benchmarks including HPS-v2.1, CLIP Score, VideoAlign, and GenEval.","one_line_summary":"DanceGRPO applies GRPO to visual generation tasks to achieve stable policy optimization across diffusion models, rectified flows, multiple tasks, and diverse reward models, outperforming prior RL methods.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That GRPO's inherent stability mechanisms can be directly adapted to overcome the optimization instabilities of prior RL methods (DDPO, DPOK) when scaling to large and diverse prompt sets in visual generation without introducing new failure modes.","pith_extraction_headline":"DanceGRPO adapts group relative policy optimization to stabilize reinforcement learning for image and video generation."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.07818/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":57,"sample":[{"doi":"","year":2020,"title":"Denoising diffusion probabilistic models","work_id":"80d27c68-54aa-49bf-b4e5-f94cb73ea612","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"High-resolution image synthesis with latent diffusion models","work_id":"3cba96fb-e636-4639-8d43-2f25ce21d4d1","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","work_id":"8034c587-fba6-4941-87ba-c98f2ac962cb","ref_index":3,"cited_arxiv_id":"2307.01952","is_internal_anchor":true},{"doi":"","year":2023,"title":"Raphael: Text- to-image generation via large mixture of diffusion paths.Advances in Neural Information Processing Systems, 36:41693–41706","work_id":"a816a099-3384-425b-9891-067a5f23cde1","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Flow Matching for Generative Modeling","work_id":"6edb71c4-5d64-40af-a394-9757ea051a36","ref_index":5,"cited_arxiv_id":"2210.02747","is_internal_anchor":true}],"resolved_work":57,"snapshot_sha256":"5a4e9e0fb9841e345dac312cd668369e29c8712df5fca9e65aa4094ae075a010","internal_anchors":21},"formal_canon":{"evidence_count":4,"snapshot_sha256":"b6ce60c4080dc5c8c8f08bce8fead91413e8ac21ad1e42ceb62e85aeb14636b3"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"f4a2c9e8-14c3-41a3-af74-d3b098f7a7fa"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:00:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PE36m4Jld/JtpLV/ptggXm4/oqbQtnHf9VLgZaYBmPzARaSO2l5cKcSyBrxGq1fZzgdPEdr5Dl572C5ltAozBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:43:34.890931Z"},"content_sha256":"0ea1f8081d2655383ed5c815c5f6246751710b57d751e33d5ff7361ebbc5fded","schema_version":"1.0","event_id":"sha256:0ea1f8081d2655383ed5c815c5f6246751710b57d751e33d5ff7361ebbc5fded"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SRUVOHSACTSVW3XXJKCF3EGTVQ/bundle.json","state_url":"https://pith.science/pith/SRUVOHSACTSVW3XXJKCF3EGTVQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SRUVOHSACTSVW3XXJKCF3EGTVQ/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":"2026-08-07T03:43:34Z","links":{"resolver":"https://pith.science/pith/SRUVOHSACTSVW3XXJKCF3EGTVQ","bundle":"https://pith.science/pith/SRUVOHSACTSVW3XXJKCF3EGTVQ/bundle.json","state":"https://pith.science/pith/SRUVOHSACTSVW3XXJKCF3EGTVQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SRUVOHSACTSVW3XXJKCF3EGTVQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SRUVOHSACTSVW3XXJKCF3EGTVQ","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":"56ad9173326fe9e6b18f0f05c6688f71c4f2d208b925a5dfba11e2965abea065","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-12T17:59:34Z","title_canon_sha256":"255a340e8bdede7b1072df20a0c8c822c889e1695410ecaaf935616224fa9107"},"schema_version":"1.0","source":{"id":"2505.07818","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.07818","created_at":"2026-07-05T12:00:49Z"},{"alias_kind":"arxiv_version","alias_value":"2505.07818v4","created_at":"2026-07-05T12:00:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.07818","created_at":"2026-07-05T12:00:49Z"},{"alias_kind":"pith_short_12","alias_value":"SRUVOHSACTSV","created_at":"2026-07-05T12:00:49Z"},{"alias_kind":"pith_short_16","alias_value":"SRUVOHSACTSVW3XX","created_at":"2026-07-05T12:00:49Z"},{"alias_kind":"pith_short_8","alias_value":"SRUVOHSA","created_at":"2026-07-05T12:00:49Z"}],"graph_snapshots":[{"event_id":"sha256:0ea1f8081d2655383ed5c815c5f6246751710b57d751e33d5ff7361ebbc5fded","target":"graph","created_at":"2026-07-05T12:00:49Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"DanceGRPO demonstrates consistent and stable policy optimization across multiple modern generative paradigms, including both diffusion models and rectified flows, maintains robust performance when scaling to complex real-world scenarios, and outperforms baseline methods by up to 181% across benchmarks including HPS-v2.1, CLIP Score, VideoAlign, and GenEval."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That GRPO's inherent stability mechanisms can be directly adapted to overcome the optimization instabilities of prior RL methods (DDPO, DPOK) when scaling to large and diverse prompt sets in visual generation without introducing new failure modes."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"DanceGRPO applies GRPO to visual generation tasks to achieve stable policy optimization across diffusion models, rectified flows, multiple tasks, and diverse reward models, outperforming prior RL methods."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"DanceGRPO adapts group relative policy optimization to stabilize reinforcement learning for image and video generation."}],"snapshot_sha256":"4398021bb665815af6bd7092e3cf784db07e3b369343db00bd12d864c4e4c068"},"formal_canon":{"evidence_count":4,"snapshot_sha256":"b6ce60c4080dc5c8c8f08bce8fead91413e8ac21ad1e42ceb62e85aeb14636b3"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.07818/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in generative AI have revolutionized visual content creation, yet aligning model outputs with human preferences remains a critical challenge. While Reinforcement Learning (RL) has emerged as a promising approach for fine-tuning generative models, existing methods like DDPO and DPOK face fundamental limitations - particularly their inability to maintain stable optimization when scaling to large and diverse prompt sets, severely restricting their practical utility. This paper presents DanceGRPO, a framework that addresses these limitations through an innovative adaptation of Grou","authors_text":"Fangyuan Kong, Jie Wu, Lingting Zhu, Mengzhao Chen, Ping Luo, Qiushan Guo, Weilin Huang, Wei Liu, Yu Gao, Zeyue Xue, Zhiheng Liu","cross_cats":[],"headline":"DanceGRPO adapts group relative policy optimization to stabilize reinforcement learning for image and video generation.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-12T17:59:34Z","title":"DanceGRPO: Unleashing GRPO on Visual Generation"},"references":{"count":57,"internal_anchors":21,"resolved_work":57,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Denoising diffusion probabilistic models","work_id":"80d27c68-54aa-49bf-b4e5-f94cb73ea612","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"High-resolution image synthesis with latent diffusion models","work_id":"3cba96fb-e636-4639-8d43-2f25ce21d4d1","year":2022},{"cited_arxiv_id":"2307.01952","doi":"","is_internal_anchor":true,"ref_index":3,"title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","work_id":"8034c587-fba6-4941-87ba-c98f2ac962cb","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Raphael: Text- to-image generation via large mixture of diffusion paths.Advances in Neural Information Processing Systems, 36:41693–41706","work_id":"a816a099-3384-425b-9891-067a5f23cde1","year":2023},{"cited_arxiv_id":"2210.02747","doi":"","is_internal_anchor":true,"ref_index":5,"title":"Flow Matching for Generative Modeling","work_id":"6edb71c4-5d64-40af-a394-9757ea051a36","year":2022}],"snapshot_sha256":"5a4e9e0fb9841e345dac312cd668369e29c8712df5fca9e65aa4094ae075a010"},"source":{"id":"2505.07818","kind":"arxiv","version":4},"verdict":{"created_at":"2026-05-11T22:23:41.241673Z","id":"f4a2c9e8-14c3-41a3-af74-d3b098f7a7fa","model_set":{"reader":"grok-4.3"},"one_line_summary":"DanceGRPO applies GRPO to visual generation tasks to achieve stable policy optimization across diffusion models, rectified flows, multiple tasks, and diverse reward models, outperforming prior RL methods.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"DanceGRPO adapts group relative policy optimization to stabilize reinforcement learning for image and video generation.","strongest_claim":"DanceGRPO demonstrates consistent and stable policy optimization across multiple modern generative paradigms, including both diffusion models and rectified flows, maintains robust performance when scaling to complex real-world scenarios, and outperforms baseline methods by up to 181% across benchmarks including HPS-v2.1, CLIP Score, VideoAlign, and GenEval.","weakest_assumption":"That GRPO's inherent stability mechanisms can be directly adapted to overcome the optimization instabilities of prior RL methods (DDPO, DPOK) when scaling to large and diverse prompt sets in visual generation without introducing new failure modes."}},"verdict_id":"f4a2c9e8-14c3-41a3-af74-d3b098f7a7fa"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:285d641d579e2188dd0aefbdf83d110f09f36026b83f17e8be13dcd9b2be1fd1","target":"record","created_at":"2026-07-05T12:00:49Z","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":"56ad9173326fe9e6b18f0f05c6688f71c4f2d208b925a5dfba11e2965abea065","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-12T17:59:34Z","title_canon_sha256":"255a340e8bdede7b1072df20a0c8c822c889e1695410ecaaf935616224fa9107"},"schema_version":"1.0","source":{"id":"2505.07818","kind":"arxiv","version":4}},"canonical_sha256":"9469571e4014e55b6ef74a845d90d3ac2c4a89e3edbc4f2264d840edb63cfaf6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9469571e4014e55b6ef74a845d90d3ac2c4a89e3edbc4f2264d840edb63cfaf6","first_computed_at":"2026-07-05T12:00:49.961801Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:00:49.961801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PCi3x8Pspnau8xOR+B4jwjqM8npca/CTQdsk6iirs7oebBwWw+ewh+oT3VC4N1YGAq+tQI4ef0GL871wRpNZAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T12:00:49.962406Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.07818","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:285d641d579e2188dd0aefbdf83d110f09f36026b83f17e8be13dcd9b2be1fd1","sha256:0ea1f8081d2655383ed5c815c5f6246751710b57d751e33d5ff7361ebbc5fded"],"state_sha256":"5052091f2b3027ed9c40fe1b93186dc30e2068e2d98218bcbcac92c533221c39"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lvXOqurqmD4zH33AWtgtj/WpVJPDOHDaMkIKvCcikYFCpSps2UyXWoq9oEwWJ2XVbzQeq18eK+4wYNInLGy2Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T03:43:34.897539Z","bundle_sha256":"15bd9b006b9dcb5fb5b732e77c350ac7e7a508057788bcd4483bc90472ce0b0e"}}