{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WKMJUTOSTJZWJ2PMCT2J4DBGJ2","short_pith_number":"pith:WKMJUTOS","canonical_record":{"source":{"id":"1910.03193","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-10-08T03:21:14Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"19440d79637affb4adfee66015557e726c4b03aea5f8ad978d4eb496c4f622fe","abstract_canon_sha256":"37938b4c5e8bb120c0c6d320b5025843878835d5f7e23d0849ce8e8e6ceddb96"},"schema_version":"1.0"},"canonical_sha256":"b2989a4dd29a7364e9ec14f49e0c264eb600310b9aad4ca462a18b6319f55ec7","source":{"kind":"arxiv","id":"1910.03193","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.03193","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"arxiv_version","alias_value":"1910.03193v3","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.03193","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"pith_short_12","alias_value":"WKMJUTOSTJZW","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WKMJUTOSTJZWJ2PM","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WKMJUTOS","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WKMJUTOSTJZWJ2PMCT2J4DBGJ2","target":"record","payload":{"canonical_record":{"source":{"id":"1910.03193","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-10-08T03:21:14Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"19440d79637affb4adfee66015557e726c4b03aea5f8ad978d4eb496c4f622fe","abstract_canon_sha256":"37938b4c5e8bb120c0c6d320b5025843878835d5f7e23d0849ce8e8e6ceddb96"},"schema_version":"1.0"},"canonical_sha256":"b2989a4dd29a7364e9ec14f49e0c264eb600310b9aad4ca462a18b6319f55ec7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:53.699430Z","signature_b64":"IwOwtdg56KMGqj3eu1D+WbqgC3TI3wuopol1uXUX51pUbJkZZ8Thp+O2y4VhyNYv+/LZoEU2vDBFKjNZj5cZBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2989a4dd29a7364e9ec14f49e0c264eb600310b9aad4ca462a18b6319f55ec7","last_reissued_at":"2026-05-17T23:38:53.698823Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:53.698823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.03193","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zpr572BMSCLwVRWDM6VmlFEfXlpPTB9BDtxGflZiGrnKQAZXoPDrbwMcKEBCZpaoI+TmsO2ZiqGRaHHCirHAAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T11:06:34.962625Z"},"content_sha256":"a4fa91db0f4aff0c05fbdd011d1cf4e7324e34813cb44286a35489fb8420fec9","schema_version":"1.0","event_id":"sha256:a4fa91db0f4aff0c05fbdd011d1cf4e7324e34813cb44286a35489fb8420fec9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WKMJUTOSTJZWJ2PMCT2J4DBGJ2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"DeepONets learn nonlinear operators from small datasets by splitting input encoding from output evaluation points.","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"George Em Karniadakis, Lu Lu, Pengzhan Jin","submitted_at":"2019-10-08T03:21:14Z","abstract_excerpt":"While it is widely known that neural networks are universal approximators of continuous functions, a less known and perhaps more powerful result is that a neural network with a single hidden layer can approximate accurately any nonlinear continuous operator. This universal approximation theorem is suggestive of the potential application of neural networks in learning nonlinear operators from data. However, the theorem guarantees only a small approximation error for a sufficient large network, and does not consider the important optimization and generalization errors. To realize this theorem in"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We propose deep operator networks (DeepONets) to learn operators accurately and efficiently from a relatively small dataset... we observe high-order error convergence in our computational tests, namely polynomial rates (from half order to fourth order) and even exponential convergence with respect to the training dataset size.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The universal approximation theorem guarantees only a small approximation error for a sufficiently large network, and does not consider the important optimization and generalization errors; the paper assumes these practical errors remain controllable with the branch-trunk split and standard training.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DeepONet learns nonlinear operators for differential equations via branch and trunk sub-networks, achieving high-order error convergence on small datasets.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"DeepONets learn nonlinear operators from small datasets by splitting input encoding from output evaluation points.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"40752807f1f16a5c15a8c012a6bb5ab3d9ca4552911126b9db676b6c5ac0d295"},"source":{"id":"1910.03193","kind":"arxiv","version":3},"verdict":{"id":"e9e894c3-73f8-474a-9205-a4ff7caaefcd","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T03:12:06.249764Z","strongest_claim":"We propose deep operator networks (DeepONets) to learn operators accurately and efficiently from a relatively small dataset... we observe high-order error convergence in our computational tests, namely polynomial rates (from half order to fourth order) and even exponential convergence with respect to the training dataset size.","one_line_summary":"DeepONet learns nonlinear operators for differential equations via branch and trunk sub-networks, achieving high-order error convergence on small datasets.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The universal approximation theorem guarantees only a small approximation error for a sufficiently large network, and does not consider the important optimization and generalization errors; the paper assumes these practical errors remain controllable with the branch-trunk split and standard training.","pith_extraction_headline":"DeepONets learn nonlinear operators from small datasets by splitting input encoding from output evaluation points."},"references":{"count":34,"sample":[{"doi":"","year":2008,"title":"L. Bottou and O. Bousquet. The tradeoﬀs of large scale learning. InAdvances in Neural Information Processing Systems, pages 161–168, 2008","work_id":"47673b81-1737-4520-91bd-724c4f4c44fa","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"S. L. Brunton, J. L. Proctor, and J. N. Kutz. Discovering governing equations from data by sparse identiﬁcation of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 113(15)","work_id":"f826ad6f-17c0-4483-a568-39693c824383","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1993,"title":"T. Chen and H. Chen. Approximations of continuous functionals by neural networks with application to dynamic systems.IEEE Transactions on Neural Networks, 4(6):910–918, 1993","work_id":"eb629682-fb82-4e90-8ebd-23bbd07e34bf","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1995,"title":"Approximationcapabilitytofunctionsofseveralvariables,nonlinearfunctionals, and operators by radial basis function neural networks","work_id":"e2d5d285-dc7f-47f9-9d8f-670c4052fb2e","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1995,"title":"T.ChenandH.Chen. Universalapproximationtononlinearoperatorsbyneuralnetworkswitharbitrary activation functions and its application to dynamical systems.IEEE Transactions on Neural Networks, 6(4):911–91","work_id":"4dd4bc81-7afb-4634-87a9-29c34b79eb47","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":34,"snapshot_sha256":"19532959f9489164d9df220a7fe1142cba9f75c7ed5a1a4b39726aa340bef3b3","internal_anchors":3},"formal_canon":{"evidence_count":2,"snapshot_sha256":"0d13591aa0d3f0a2e4dcec814a020484f4ba69479275d5d3b78db9e4093b2dc6"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"e9e894c3-73f8-474a-9205-a4ff7caaefcd"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/zWDUN4TORACm7/HUT/UMVX+wB9li1CSn7zvAF9CqqUZ2WJ4dighY84bSPdqh5rDFRbtPXblLKRFtu2BvHDoAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T11:06:34.964039Z"},"content_sha256":"136b088da2cd5695c97445ea036c6d299f1cab2a2cf465262d0e29bacb5b9b2a","schema_version":"1.0","event_id":"sha256:136b088da2cd5695c97445ea036c6d299f1cab2a2cf465262d0e29bacb5b9b2a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WKMJUTOSTJZWJ2PMCT2J4DBGJ2/bundle.json","state_url":"https://pith.science/pith/WKMJUTOSTJZWJ2PMCT2J4DBGJ2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WKMJUTOSTJZWJ2PMCT2J4DBGJ2/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":"2026-06-06T11:06:34Z","links":{"resolver":"https://pith.science/pith/WKMJUTOSTJZWJ2PMCT2J4DBGJ2","bundle":"https://pith.science/pith/WKMJUTOSTJZWJ2PMCT2J4DBGJ2/bundle.json","state":"https://pith.science/pith/WKMJUTOSTJZWJ2PMCT2J4DBGJ2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WKMJUTOSTJZWJ2PMCT2J4DBGJ2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WKMJUTOSTJZWJ2PMCT2J4DBGJ2","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":"37938b4c5e8bb120c0c6d320b5025843878835d5f7e23d0849ce8e8e6ceddb96","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-10-08T03:21:14Z","title_canon_sha256":"19440d79637affb4adfee66015557e726c4b03aea5f8ad978d4eb496c4f622fe"},"schema_version":"1.0","source":{"id":"1910.03193","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.03193","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"arxiv_version","alias_value":"1910.03193v3","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.03193","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"pith_short_12","alias_value":"WKMJUTOSTJZW","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WKMJUTOSTJZWJ2PM","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WKMJUTOS","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:136b088da2cd5695c97445ea036c6d299f1cab2a2cf465262d0e29bacb5b9b2a","target":"graph","created_at":"2026-05-17T23:38:53Z","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":"We propose deep operator networks (DeepONets) to learn operators accurately and efficiently from a relatively small dataset... we observe high-order error convergence in our computational tests, namely polynomial rates (from half order to fourth order) and even exponential convergence with respect to the training dataset size."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The universal approximation theorem guarantees only a small approximation error for a sufficiently large network, and does not consider the important optimization and generalization errors; the paper assumes these practical errors remain controllable with the branch-trunk split and standard training."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"DeepONet learns nonlinear operators for differential equations via branch and trunk sub-networks, achieving high-order error convergence on small datasets."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"DeepONets learn nonlinear operators from small datasets by splitting input encoding from output evaluation points."}],"snapshot_sha256":"40752807f1f16a5c15a8c012a6bb5ab3d9ca4552911126b9db676b6c5ac0d295"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"0d13591aa0d3f0a2e4dcec814a020484f4ba69479275d5d3b78db9e4093b2dc6"},"paper":{"abstract_excerpt":"While it is widely known that neural networks are universal approximators of continuous functions, a less known and perhaps more powerful result is that a neural network with a single hidden layer can approximate accurately any nonlinear continuous operator. This universal approximation theorem is suggestive of the potential application of neural networks in learning nonlinear operators from data. However, the theorem guarantees only a small approximation error for a sufficient large network, and does not consider the important optimization and generalization errors. To realize this theorem in","authors_text":"George Em Karniadakis, Lu Lu, Pengzhan Jin","cross_cats":["stat.ML"],"headline":"DeepONets learn nonlinear operators from small datasets by splitting input encoding from output evaluation points.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-10-08T03:21:14Z","title":"DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators"},"references":{"count":34,"internal_anchors":3,"resolved_work":34,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"L. Bottou and O. Bousquet. The tradeoﬀs of large scale learning. InAdvances in Neural Information Processing Systems, pages 161–168, 2008","work_id":"47673b81-1737-4520-91bd-724c4f4c44fa","year":2008},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"S. L. Brunton, J. L. Proctor, and J. N. Kutz. Discovering governing equations from data by sparse identiﬁcation of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 113(15)","work_id":"f826ad6f-17c0-4483-a568-39693c824383","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"T. Chen and H. Chen. Approximations of continuous functionals by neural networks with application to dynamic systems.IEEE Transactions on Neural Networks, 4(6):910–918, 1993","work_id":"eb629682-fb82-4e90-8ebd-23bbd07e34bf","year":1993},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Approximationcapabilitytofunctionsofseveralvariables,nonlinearfunctionals, and operators by radial basis function neural networks","work_id":"e2d5d285-dc7f-47f9-9d8f-670c4052fb2e","year":1995},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"T.ChenandH.Chen. Universalapproximationtononlinearoperatorsbyneuralnetworkswitharbitrary activation functions and its application to dynamical systems.IEEE Transactions on Neural Networks, 6(4):911–91","work_id":"4dd4bc81-7afb-4634-87a9-29c34b79eb47","year":1995}],"snapshot_sha256":"19532959f9489164d9df220a7fe1142cba9f75c7ed5a1a4b39726aa340bef3b3"},"source":{"id":"1910.03193","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-15T03:12:06.249764Z","id":"e9e894c3-73f8-474a-9205-a4ff7caaefcd","model_set":{"reader":"grok-4.3"},"one_line_summary":"DeepONet learns nonlinear operators for differential equations via branch and trunk sub-networks, achieving high-order error convergence on small datasets.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"DeepONets learn nonlinear operators from small datasets by splitting input encoding from output evaluation points.","strongest_claim":"We propose deep operator networks (DeepONets) to learn operators accurately and efficiently from a relatively small dataset... we observe high-order error convergence in our computational tests, namely polynomial rates (from half order to fourth order) and even exponential convergence with respect to the training dataset size.","weakest_assumption":"The universal approximation theorem guarantees only a small approximation error for a sufficiently large network, and does not consider the important optimization and generalization errors; the paper assumes these practical errors remain controllable with the branch-trunk split and standard training."}},"verdict_id":"e9e894c3-73f8-474a-9205-a4ff7caaefcd"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a4fa91db0f4aff0c05fbdd011d1cf4e7324e34813cb44286a35489fb8420fec9","target":"record","created_at":"2026-05-17T23:38:53Z","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":"37938b4c5e8bb120c0c6d320b5025843878835d5f7e23d0849ce8e8e6ceddb96","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-10-08T03:21:14Z","title_canon_sha256":"19440d79637affb4adfee66015557e726c4b03aea5f8ad978d4eb496c4f622fe"},"schema_version":"1.0","source":{"id":"1910.03193","kind":"arxiv","version":3}},"canonical_sha256":"b2989a4dd29a7364e9ec14f49e0c264eb600310b9aad4ca462a18b6319f55ec7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b2989a4dd29a7364e9ec14f49e0c264eb600310b9aad4ca462a18b6319f55ec7","first_computed_at":"2026-05-17T23:38:53.698823Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:53.698823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IwOwtdg56KMGqj3eu1D+WbqgC3TI3wuopol1uXUX51pUbJkZZ8Thp+O2y4VhyNYv+/LZoEU2vDBFKjNZj5cZBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:53.699430Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.03193","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a4fa91db0f4aff0c05fbdd011d1cf4e7324e34813cb44286a35489fb8420fec9","sha256:136b088da2cd5695c97445ea036c6d299f1cab2a2cf465262d0e29bacb5b9b2a"],"state_sha256":"d31e092df47db283b5e96f7c82798f35c3b2caf6f309d1c4e8c8c2570dc7875c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KFALdo3Frfj0wMUw6dKE+8hh2u5oeHoynKupe4olwbWrcM8TBkMdz3kErsZFHADOB1HhyKESOb3AlVrjXJUADw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T11:06:34.969504Z","bundle_sha256":"7f1eeabb86400f26a2688bca4ec3c0e1d61c151ba9d89848d4539bcf40573770"}}