{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:PREMJTZC4J7RNK4IHH6M6UBNEE","short_pith_number":"pith:PREMJTZC","canonical_record":{"source":{"id":"2309.16671","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T17:59:56Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"77371d4b9df8c37f41b4553938b8a1d5762fff9a02903ac4b560ddeca04e5b06","abstract_canon_sha256":"ebf6224ad45b03c9907c6c2a98803e7c9116e2a50f09fa513efa5dcd022d1323"},"schema_version":"1.0"},"canonical_sha256":"7c48c4cf22e27f16ab8839fccf502d21084bf52b5499072da4555157a99911e5","source":{"kind":"arxiv","id":"2309.16671","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.16671","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"arxiv_version","alias_value":"2309.16671v6","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.16671","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"pith_short_12","alias_value":"PREMJTZC4J7R","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"PREMJTZC4J7RNK4I","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"PREMJTZC","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:PREMJTZC4J7RNK4IHH6M6UBNEE","target":"record","payload":{"canonical_record":{"source":{"id":"2309.16671","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T17:59:56Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"77371d4b9df8c37f41b4553938b8a1d5762fff9a02903ac4b560ddeca04e5b06","abstract_canon_sha256":"ebf6224ad45b03c9907c6c2a98803e7c9116e2a50f09fa513efa5dcd022d1323"},"schema_version":"1.0"},"canonical_sha256":"7c48c4cf22e27f16ab8839fccf502d21084bf52b5499072da4555157a99911e5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:48.379391Z","signature_b64":"igPPM9GWA+Lz96Kbq4pXGWKsIAZzNR/ShlPJ51kj3TzD/62M0f/B0VkGogAhaMX1cOxGXxH5uGdRYHHxvqixCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c48c4cf22e27f16ab8839fccf502d21084bf52b5499072da4555157a99911e5","last_reissued_at":"2026-05-17T23:38:48.378631Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:48.378631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.16671","source_version":6,"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":"fmDgskdi9XKa0VUpAZtS0at2oba0LTVHw0Qki6eIzjJ813jhHU/Tg6rvaryoFyrOCmSmFWwh/C7QLuBUom2/Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T03:52:23.837619Z"},"content_sha256":"b2637fdb040de6689b398a7651bc8c949495f5a558bbdc21c39dbbf81019ca92","schema_version":"1.0","event_id":"sha256:b2637fdb040de6689b398a7651bc8c949495f5a558bbdc21c39dbbf81019ca92"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:PREMJTZC4J7RNK4IHH6M6UBNEE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Demystifying CLIP Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"MetaCLIP balances CommonCrawl image-text pairs using CLIP-derived metadata to exceed original CLIP performance on zero-shot benchmarks.","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Christoph Feichtenhofer, Gargi Ghosh, Hu Xu, Luke Zettlemoyer, Po-Yao Huang, Russell Howes, Saining Xie, Shang-Wen Li, Vasu Sharma, Xiaoqing Ellen Tan","submitted_at":"2023-09-28T17:59:56Z","abstract_excerpt":"Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative models. We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective. However, CLIP only provides very limited information about its data and how it has been collected, leading to works that aim to reproduce CLIP's data by filtering with its model parameters. In this work, we intend to reveal CLIP's data curation approach and in our pursuit of making it o"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"MetaCLIP applied to CommonCrawl with 400M image-text data pairs outperforms CLIP's data on multiple standard benchmarks. In zero-shot ImageNet classification, MetaCLIP achieves 70.8% accuracy, surpassing CLIP's 68.3% on ViT-B models. Scaling to 1B data attains 72.4%.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That metadata derived from CLIP's own concepts is sufficient to capture the key distributional properties that made CLIP data effective, and that explicit balancing over this metadata is the primary driver of the observed gains rather than other unmeasured factors in the raw pool.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MetaCLIP curates balanced 400M-pair subsets from CommonCrawl that outperform CLIP data, reaching 70.8% zero-shot ImageNet accuracy on ViT-B versus CLIP's 68.3%.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MetaCLIP balances CommonCrawl image-text pairs using CLIP-derived metadata to exceed original CLIP performance on zero-shot benchmarks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"0d269e3a356b6884149179892cfac490ad24009d4a26d1eccc834cf3b5a3abf5"},"source":{"id":"2309.16671","kind":"arxiv","version":6},"verdict":{"id":"684ca178-8eed-4ae5-8192-451ab796bca5","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T09:16:41.611459Z","strongest_claim":"MetaCLIP applied to CommonCrawl with 400M image-text data pairs outperforms CLIP's data on multiple standard benchmarks. In zero-shot ImageNet classification, MetaCLIP achieves 70.8% accuracy, surpassing CLIP's 68.3% on ViT-B models. Scaling to 1B data attains 72.4%.","one_line_summary":"MetaCLIP curates balanced 400M-pair subsets from CommonCrawl that outperform CLIP data, reaching 70.8% zero-shot ImageNet accuracy on ViT-B versus CLIP's 68.3%.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That metadata derived from CLIP's own concepts is sufficient to capture the key distributional properties that made CLIP data effective, and that explicit balancing over this metadata is the primary driver of the observed gains rather than other unmeasured factors in the raw pool.","pith_extraction_headline":"MetaCLIP balances CommonCrawl image-text pairs using CLIP-derived metadata to exceed original CLIP performance on zero-shot benchmarks."},"references":{"count":179,"sample":[{"doi":"","year":2015,"title":"Coresets for nonparametric estimation-the case of dp-means","work_id":"2effacca-ca04-40bb-95d7-c6d934b4b7a8","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"An image is worth 16x16 words: Transformers for image recognition at scale","work_id":"282f8133-1b3d-4933-b68c-70ee9cdd289e","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2011,"title":"Scalable training of mixture models via coresets","work_id":"0087f78b-4086-46af-9166-7fc8265f8325","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Datacomp: In search of the next generation of multimodal datasets, 2023","work_id":"6b216263-81b4-4644-bf69-bb67cde04476","ref_index":6,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2004,"title":"On coresets for k-means and k-median clustering","work_id":"c9a1491f-4ac1-4e2c-84bf-3c5b50a08fe6","ref_index":7,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":179,"snapshot_sha256":"8b35deeb41bf4fb875ca75dd7d03d92a854a79d01654aca3d5dd04ac9bc33aa5","internal_anchors":33},"formal_canon":{"evidence_count":2,"snapshot_sha256":"1b591dced2f862b76482ace20541da88ba62ce22ff3c9eaa323c0eac25eb8ae1"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"684ca178-8eed-4ae5-8192-451ab796bca5"},"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":"8MykNFikjUBtYVwXr+3DwbkLPFTvp00MGwAxKHtxr9bpD2r+bXlE0Yja669AzbgiTGqJLJzEQVlZfGplShTBDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T03:52:23.838638Z"},"content_sha256":"b7b864cf5063f82db27a913b6dd5892eb428ea5e9960f4872d45c44e04f37486","schema_version":"1.0","event_id":"sha256:b7b864cf5063f82db27a913b6dd5892eb428ea5e9960f4872d45c44e04f37486"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PREMJTZC4J7RNK4IHH6M6UBNEE/bundle.json","state_url":"https://pith.science/pith/PREMJTZC4J7RNK4IHH6M6UBNEE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PREMJTZC4J7RNK4IHH6M6UBNEE/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":"2026-05-27T03:52:23Z","links":{"resolver":"https://pith.science/pith/PREMJTZC4J7RNK4IHH6M6UBNEE","bundle":"https://pith.science/pith/PREMJTZC4J7RNK4IHH6M6UBNEE/bundle.json","state":"https://pith.science/pith/PREMJTZC4J7RNK4IHH6M6UBNEE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PREMJTZC4J7RNK4IHH6M6UBNEE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:PREMJTZC4J7RNK4IHH6M6UBNEE","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":"ebf6224ad45b03c9907c6c2a98803e7c9116e2a50f09fa513efa5dcd022d1323","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T17:59:56Z","title_canon_sha256":"77371d4b9df8c37f41b4553938b8a1d5762fff9a02903ac4b560ddeca04e5b06"},"schema_version":"1.0","source":{"id":"2309.16671","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.16671","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"arxiv_version","alias_value":"2309.16671v6","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.16671","created_at":"2026-05-17T23:38:48Z"},{"alias_kind":"pith_short_12","alias_value":"PREMJTZC4J7R","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"PREMJTZC4J7RNK4I","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"PREMJTZC","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:b7b864cf5063f82db27a913b6dd5892eb428ea5e9960f4872d45c44e04f37486","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":"MetaCLIP applied to CommonCrawl with 400M image-text data pairs outperforms CLIP's data on multiple standard benchmarks. In zero-shot ImageNet classification, MetaCLIP achieves 70.8% accuracy, surpassing CLIP's 68.3% on ViT-B models. Scaling to 1B data attains 72.4%."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That metadata derived from CLIP's own concepts is sufficient to capture the key distributional properties that made CLIP data effective, and that explicit balancing over this metadata is the primary driver of the observed gains rather than other unmeasured factors in the raw pool."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MetaCLIP curates balanced 400M-pair subsets from CommonCrawl that outperform CLIP data, reaching 70.8% zero-shot ImageNet accuracy on ViT-B versus CLIP's 68.3%."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"MetaCLIP balances CommonCrawl image-text pairs using CLIP-derived metadata to exceed original CLIP performance on zero-shot benchmarks."}],"snapshot_sha256":"0d269e3a356b6884149179892cfac490ad24009d4a26d1eccc834cf3b5a3abf5"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"1b591dced2f862b76482ace20541da88ba62ce22ff3c9eaa323c0eac25eb8ae1"},"paper":{"abstract_excerpt":"Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative models. We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective. However, CLIP only provides very limited information about its data and how it has been collected, leading to works that aim to reproduce CLIP's data by filtering with its model parameters. In this work, we intend to reveal CLIP's data curation approach and in our pursuit of making it o","authors_text":"Christoph Feichtenhofer, Gargi Ghosh, Hu Xu, Luke Zettlemoyer, Po-Yao Huang, Russell Howes, Saining Xie, Shang-Wen Li, Vasu Sharma, Xiaoqing Ellen Tan","cross_cats":["cs.CL"],"headline":"MetaCLIP balances CommonCrawl image-text pairs using CLIP-derived metadata to exceed original CLIP performance on zero-shot benchmarks.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T17:59:56Z","title":"Demystifying CLIP Data"},"references":{"count":179,"internal_anchors":33,"resolved_work":179,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Coresets for nonparametric estimation-the case of dp-means","work_id":"2effacca-ca04-40bb-95d7-c6d934b4b7a8","year":2015},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"An image is worth 16x16 words: Transformers for image recognition at scale","work_id":"282f8133-1b3d-4933-b68c-70ee9cdd289e","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Scalable training of mixture models via coresets","work_id":"0087f78b-4086-46af-9166-7fc8265f8325","year":2011},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":6,"title":"Datacomp: In search of the next generation of multimodal datasets, 2023","work_id":"6b216263-81b4-4644-bf69-bb67cde04476","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":7,"title":"On coresets for k-means and k-median clustering","work_id":"c9a1491f-4ac1-4e2c-84bf-3c5b50a08fe6","year":2004}],"snapshot_sha256":"8b35deeb41bf4fb875ca75dd7d03d92a854a79d01654aca3d5dd04ac9bc33aa5"},"source":{"id":"2309.16671","kind":"arxiv","version":6},"verdict":{"created_at":"2026-05-16T09:16:41.611459Z","id":"684ca178-8eed-4ae5-8192-451ab796bca5","model_set":{"reader":"grok-4.3"},"one_line_summary":"MetaCLIP curates balanced 400M-pair subsets from CommonCrawl that outperform CLIP data, reaching 70.8% zero-shot ImageNet accuracy on ViT-B versus CLIP's 68.3%.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"MetaCLIP balances CommonCrawl image-text pairs using CLIP-derived metadata to exceed original CLIP performance on zero-shot benchmarks.","strongest_claim":"MetaCLIP applied to CommonCrawl with 400M image-text data pairs outperforms CLIP's data on multiple standard benchmarks. In zero-shot ImageNet classification, MetaCLIP achieves 70.8% accuracy, surpassing CLIP's 68.3% on ViT-B models. Scaling to 1B data attains 72.4%.","weakest_assumption":"That metadata derived from CLIP's own concepts is sufficient to capture the key distributional properties that made CLIP data effective, and that explicit balancing over this metadata is the primary driver of the observed gains rather than other unmeasured factors in the raw pool."}},"verdict_id":"684ca178-8eed-4ae5-8192-451ab796bca5"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b2637fdb040de6689b398a7651bc8c949495f5a558bbdc21c39dbbf81019ca92","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":"ebf6224ad45b03c9907c6c2a98803e7c9116e2a50f09fa513efa5dcd022d1323","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T17:59:56Z","title_canon_sha256":"77371d4b9df8c37f41b4553938b8a1d5762fff9a02903ac4b560ddeca04e5b06"},"schema_version":"1.0","source":{"id":"2309.16671","kind":"arxiv","version":6}},"canonical_sha256":"7c48c4cf22e27f16ab8839fccf502d21084bf52b5499072da4555157a99911e5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c48c4cf22e27f16ab8839fccf502d21084bf52b5499072da4555157a99911e5","first_computed_at":"2026-05-17T23:38:48.378631Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:48.378631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"igPPM9GWA+Lz96Kbq4pXGWKsIAZzNR/ShlPJ51kj3TzD/62M0f/B0VkGogAhaMX1cOxGXxH5uGdRYHHxvqixCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:48.379391Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.16671","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b2637fdb040de6689b398a7651bc8c949495f5a558bbdc21c39dbbf81019ca92","sha256:b7b864cf5063f82db27a913b6dd5892eb428ea5e9960f4872d45c44e04f37486"],"state_sha256":"48f93fdda4c5b438f29feb8fa7ff09c646b41f9e985ea554071ddb3192f4cb96"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NDTf3WU2uZGD+hjKuJN3l2OK0Ji588iUOF7cuCd8JD9jAf+sSXMr8TwWmh5KpRhtC+JHZkS6Wk/i2F6ZAIBEBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T03:52:23.842921Z","bundle_sha256":"85453cca6fee6ab0edcb3738660594b1144370bd0ad0312bfe888569c9191ee4"}}