{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:B23NNIRAURJHBUVN34I5D3U5OL","short_pith_number":"pith:B23NNIRA","canonical_record":{"source":{"id":"2111.11432","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-22T18:59:55Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f13e6641fc1573c2d91642722d547a7e5498ba2564e64461cfe9da4225b316b9","abstract_canon_sha256":"5e7783cbf1d0a2462168b4970fb7b1153d93742dd524e7d221acd1f10c61986e"},"schema_version":"1.0"},"canonical_sha256":"0eb6d6a220a45270d2addf11d1ee9d72e920c48847c2d38f22f5ed4161e5c655","source":{"kind":"arxiv","id":"2111.11432","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.11432","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"arxiv_version","alias_value":"2111.11432v1","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.11432","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"pith_short_12","alias_value":"B23NNIRAURJH","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"B23NNIRAURJHBUVN","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"B23NNIRA","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:B23NNIRAURJHBUVN34I5D3U5OL","target":"record","payload":{"canonical_record":{"source":{"id":"2111.11432","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-22T18:59:55Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f13e6641fc1573c2d91642722d547a7e5498ba2564e64461cfe9da4225b316b9","abstract_canon_sha256":"5e7783cbf1d0a2462168b4970fb7b1153d93742dd524e7d221acd1f10c61986e"},"schema_version":"1.0"},"canonical_sha256":"0eb6d6a220a45270d2addf11d1ee9d72e920c48847c2d38f22f5ed4161e5c655","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:48.319197Z","signature_b64":"TzeC7DbXVM7D/CJb51DwPyaiGbTm9VltevQXEjQ3hX/U16Ov7jTPbuWSTaevfPpWJIga8ZKU8AQd5lIMgHtZCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0eb6d6a220a45270d2addf11d1ee9d72e920c48847c2d38f22f5ed4161e5c655","last_reissued_at":"2026-05-17T23:38:48.318672Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:48.318672Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.11432","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":"2026-05-17T23:38:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WAFzUQKVVKeMnZDYARM96moSw88Pf4+Reks69l3lNr/W86NKFK6Dyt3L/Wks5zRyeZ1RZG6CxHMOrndQHu3SDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T11:11:42.703727Z"},"content_sha256":"6bb63ebdbff528948a7903d25877bd9c023954a2e8a303c5e3fecc346e9ad4cd","schema_version":"1.0","event_id":"sha256:6bb63ebdbff528948a7903d25877bd9c023954a2e8a303c5e3fecc346e9ad4cd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:B23NNIRAURJHBUVN34I5D3U5OL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Florence: A New Foundation Model for Computer Vision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Florence expands vision models from coarse scene representations to fine objects, videos, and extra modalities like depth using web-scale image-text data.","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Bin Xiao, Boxin Li, Ce Liu, Chunyuan Li, Dongdong Chen, Houdong Hu, Jianfeng Gao, Jianfeng Wang, Jianwei Yang, Lijuan Wang, Luowei Zhou, Lu Yuan, Mengchen Liu, Michael Zeng, Noel Codella, Pengchuan Zhang, Xiyang Dai, Xuedong Huang, Yi-Ling Chen, Yumao Lu, Yu Shi, Zhen Xiao, Zicheng Liu","submitted_at":"2021-11-22T18:59:55Z","abstract_excerpt":"Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications. While existing vision foundation models such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a new co"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Florence achieves new state-of-the-art results in majority of 44 representative benchmarks, e.g., ImageNet-1K zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of 97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That training on diverse web-scale image-text data produces representations that generalize well with minimal customization across static images, videos, fine-grained objects, and additional modalities such as depth and captions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Florence is a new vision foundation model that learns universal visual-language representations from web-scale data and reports state-of-the-art results on 44 benchmarks including 83.74% zero-shot ImageNet top-1 accuracy.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Florence expands vision models from coarse scene representations to fine objects, videos, and extra modalities like depth using web-scale image-text data.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"87f0bfda7999dce8fd1d30086cf44b31166aedee730879cc1a45d7837f7f3c36"},"source":{"id":"2111.11432","kind":"arxiv","version":1},"verdict":{"id":"ab98a41c-a54b-4d45-90a3-3fbd694714d9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T09:34:47.258359Z","strongest_claim":"Florence achieves new state-of-the-art results in majority of 44 representative benchmarks, e.g., ImageNet-1K zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of 97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600.","one_line_summary":"Florence is a new vision foundation model that learns universal visual-language representations from web-scale data and reports state-of-the-art results on 44 benchmarks including 83.74% zero-shot ImageNet top-1 accuracy.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That training on diverse web-scale image-text data produces representations that generalize well with minimal customization across static images, videos, fine-grained objects, and additional modalities such as depth and captions.","pith_extraction_headline":"Florence expands vision models from coarse scene representations to fine objects, videos, and extra modalities like depth using web-scale image-text data."},"references":{"count":28,"sample":[{"doi":"","year":2014,"title":"Berg, T., Liu, J., Lee, S. W., Alexander, M. L., Jacobs, D. W., and Belhumeur, P. N. Birdsnap: Large-scale ﬁne-grained visual categorization of birds. In 2014 IEEE Conference on Computer Vision and Pa","work_id":"87448d35-f56c-45c8-bf0f-ba7658ed8a92","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"On the Opportunities and Risks of Foundation Models","work_id":"a18039e9-928d-47c9-a836-32656a71bf71","ref_index":2,"cited_arxiv_id":"2108.07258","is_internal_anchor":true},{"doi":"","year":2005,"title":"Language Models are Few-Shot Learners","work_id":"214732c0-2edd-44a0-af9e-28184a2b8279","ref_index":3,"cited_arxiv_id":"2005.14165","is_internal_anchor":true},{"doi":"","year":2011,"title":"Learning the best pooling strategy for visual semantic embedding","work_id":"bd434e69-b4cc-4f87-b065-4514e012198d","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2009,"title":"Coatnet: Marrying convolution and attention for all data sizes","work_id":"08de913e-bf2d-4981-af29-094eb833d77d","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":28,"snapshot_sha256":"e4cd87d0265b1844de49066fe7502a76bd4a56534b52da8baa63ce7828ae8ba7","internal_anchors":9},"formal_canon":{"evidence_count":2,"snapshot_sha256":"8a2319d140f6fd89b0e860deba9b9df7fd2f5db02aad20fb7a2fa51e1dec4b78"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"ab98a41c-a54b-4d45-90a3-3fbd694714d9"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HsmAxyI9IT/KpsqYCOB1vkCFqro08Yp9VQd8ejbSgNiVSAzvLm4zoYfa+z2m/DswSyCiNGIeQCUDYDbSENhuDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T11:11:42.704386Z"},"content_sha256":"3b39ab08bcf4601fedc118fc17178c551c29cff4903fbab4786c48e34e040687","schema_version":"1.0","event_id":"sha256:3b39ab08bcf4601fedc118fc17178c551c29cff4903fbab4786c48e34e040687"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B23NNIRAURJHBUVN34I5D3U5OL/bundle.json","state_url":"https://pith.science/pith/B23NNIRAURJHBUVN34I5D3U5OL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B23NNIRAURJHBUVN34I5D3U5OL/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":"2026-06-04T11:11:42Z","links":{"resolver":"https://pith.science/pith/B23NNIRAURJHBUVN34I5D3U5OL","bundle":"https://pith.science/pith/B23NNIRAURJHBUVN34I5D3U5OL/bundle.json","state":"https://pith.science/pith/B23NNIRAURJHBUVN34I5D3U5OL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B23NNIRAURJHBUVN34I5D3U5OL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:B23NNIRAURJHBUVN34I5D3U5OL","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":"5e7783cbf1d0a2462168b4970fb7b1153d93742dd524e7d221acd1f10c61986e","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-22T18:59:55Z","title_canon_sha256":"f13e6641fc1573c2d91642722d547a7e5498ba2564e64461cfe9da4225b316b9"},"schema_version":"1.0","source":{"id":"2111.11432","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.11432","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"arxiv_version","alias_value":"2111.11432v1","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.11432","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"pith_short_12","alias_value":"B23NNIRAURJH","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"B23NNIRAURJHBUVN","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"B23NNIRA","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:3b39ab08bcf4601fedc118fc17178c551c29cff4903fbab4786c48e34e040687","target":"graph","created_at":"2026-05-17T23:38:48Z","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":"Florence achieves new state-of-the-art results in majority of 44 representative benchmarks, e.g., ImageNet-1K zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of 97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That training on diverse web-scale image-text data produces representations that generalize well with minimal customization across static images, videos, fine-grained objects, and additional modalities such as depth and captions."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Florence is a new vision foundation model that learns universal visual-language representations from web-scale data and reports state-of-the-art results on 44 benchmarks including 83.74% zero-shot ImageNet top-1 accuracy."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Florence expands vision models from coarse scene representations to fine objects, videos, and extra modalities like depth using web-scale image-text data."}],"snapshot_sha256":"87f0bfda7999dce8fd1d30086cf44b31166aedee730879cc1a45d7837f7f3c36"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"8a2319d140f6fd89b0e860deba9b9df7fd2f5db02aad20fb7a2fa51e1dec4b78"},"paper":{"abstract_excerpt":"Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications. While existing vision foundation models such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a new co","authors_text":"Bin Xiao, Boxin Li, Ce Liu, Chunyuan Li, Dongdong Chen, Houdong Hu, Jianfeng Gao, Jianfeng Wang, Jianwei Yang, Lijuan Wang, Luowei Zhou, Lu Yuan, Mengchen Liu, Michael Zeng, Noel Codella, Pengchuan Zhang, Xiyang Dai, Xuedong Huang, Yi-Ling Chen, Yumao Lu, Yu Shi, Zhen Xiao, Zicheng Liu","cross_cats":["cs.AI","cs.LG"],"headline":"Florence expands vision models from coarse scene representations to fine objects, videos, and extra modalities like depth using web-scale image-text data.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-22T18:59:55Z","title":"Florence: A New Foundation Model for Computer Vision"},"references":{"count":28,"internal_anchors":9,"resolved_work":28,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Berg, T., Liu, J., Lee, S. W., Alexander, M. L., Jacobs, D. W., and Belhumeur, P. N. Birdsnap: Large-scale ﬁne-grained visual categorization of birds. In 2014 IEEE Conference on Computer Vision and Pa","work_id":"87448d35-f56c-45c8-bf0f-ba7658ed8a92","year":2014},{"cited_arxiv_id":"2108.07258","doi":"","is_internal_anchor":true,"ref_index":2,"title":"On the Opportunities and Risks of Foundation Models","work_id":"a18039e9-928d-47c9-a836-32656a71bf71","year":null},{"cited_arxiv_id":"2005.14165","doi":"","is_internal_anchor":true,"ref_index":3,"title":"Language Models are Few-Shot Learners","work_id":"214732c0-2edd-44a0-af9e-28184a2b8279","year":2005},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Learning the best pooling strategy for visual semantic embedding","work_id":"bd434e69-b4cc-4f87-b065-4514e012198d","year":2011},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Coatnet: Marrying convolution and attention for all data sizes","work_id":"08de913e-bf2d-4981-af29-094eb833d77d","year":2009}],"snapshot_sha256":"e4cd87d0265b1844de49066fe7502a76bd4a56534b52da8baa63ce7828ae8ba7"},"source":{"id":"2111.11432","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-16T09:34:47.258359Z","id":"ab98a41c-a54b-4d45-90a3-3fbd694714d9","model_set":{"reader":"grok-4.3"},"one_line_summary":"Florence is a new vision foundation model that learns universal visual-language representations from web-scale data and reports state-of-the-art results on 44 benchmarks including 83.74% zero-shot ImageNet top-1 accuracy.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Florence expands vision models from coarse scene representations to fine objects, videos, and extra modalities like depth using web-scale image-text data.","strongest_claim":"Florence achieves new state-of-the-art results in majority of 44 representative benchmarks, e.g., ImageNet-1K zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of 97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600.","weakest_assumption":"That training on diverse web-scale image-text data produces representations that generalize well with minimal customization across static images, videos, fine-grained objects, and additional modalities such as depth and captions."}},"verdict_id":"ab98a41c-a54b-4d45-90a3-3fbd694714d9"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6bb63ebdbff528948a7903d25877bd9c023954a2e8a303c5e3fecc346e9ad4cd","target":"record","created_at":"2026-05-17T23:38:48Z","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":"5e7783cbf1d0a2462168b4970fb7b1153d93742dd524e7d221acd1f10c61986e","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-22T18:59:55Z","title_canon_sha256":"f13e6641fc1573c2d91642722d547a7e5498ba2564e64461cfe9da4225b316b9"},"schema_version":"1.0","source":{"id":"2111.11432","kind":"arxiv","version":1}},"canonical_sha256":"0eb6d6a220a45270d2addf11d1ee9d72e920c48847c2d38f22f5ed4161e5c655","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0eb6d6a220a45270d2addf11d1ee9d72e920c48847c2d38f22f5ed4161e5c655","first_computed_at":"2026-05-17T23:38:48.318672Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:48.318672Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TzeC7DbXVM7D/CJb51DwPyaiGbTm9VltevQXEjQ3hX/U16Ov7jTPbuWSTaevfPpWJIga8ZKU8AQd5lIMgHtZCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:48.319197Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.11432","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6bb63ebdbff528948a7903d25877bd9c023954a2e8a303c5e3fecc346e9ad4cd","sha256:3b39ab08bcf4601fedc118fc17178c551c29cff4903fbab4786c48e34e040687"],"state_sha256":"2136a6104fd08136151222763ad712fb40761c1eb5741b722e8603105339893a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KU0sG1xQq/GKG1nCqq5jBoOIz29Tuuq0/inN4o3njz9F+Zp8Lm/y0NiblA/bZvvT5kB94Vz25J9tnky8k9kgDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T11:11:42.707105Z","bundle_sha256":"a879954367a62ffc7543de802dcb50c95527c71fe2413023ec18fa1363ac31d2"}}