{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:DUUODIEZCIUEGTXOCY3O3FZQAD","short_pith_number":"pith:DUUODIEZ","canonical_record":{"source":{"id":"2605.12553","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-11T07:58:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"91f834b227a7d9d06583b81c501031b01223c077a03749b51ca3b1382e860488","abstract_canon_sha256":"e6b2d9a67c83a62e245be874c87f9cf5a19a5e8901a43ed9dd2d2e3655af8e68"},"schema_version":"1.0"},"canonical_sha256":"1d28e1a0991228434eee1636ed973000fd0d5aeeb82fd236a129edd7f753c69f","source":{"kind":"arxiv","id":"2605.12553","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12553","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12553v1","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12553","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"pith_short_12","alias_value":"DUUODIEZCIUE","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"DUUODIEZCIUEGTXO","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"DUUODIEZ","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:DUUODIEZCIUEGTXOCY3O3FZQAD","target":"record","payload":{"canonical_record":{"source":{"id":"2605.12553","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-11T07:58:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"91f834b227a7d9d06583b81c501031b01223c077a03749b51ca3b1382e860488","abstract_canon_sha256":"e6b2d9a67c83a62e245be874c87f9cf5a19a5e8901a43ed9dd2d2e3655af8e68"},"schema_version":"1.0"},"canonical_sha256":"1d28e1a0991228434eee1636ed973000fd0d5aeeb82fd236a129edd7f753c69f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:10:02.098260Z","signature_b64":"EPnUbgc7ofLhar/uKOFmxGHrdWrYnFWeaRyaigr1qi30zeF1qG8UcAmtugz7lxDG2Gxpe5tiR1nKJPi7CLJNDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d28e1a0991228434eee1636ed973000fd0d5aeeb82fd236a129edd7f753c69f","last_reissued_at":"2026-05-18T03:10:02.097730Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:10:02.097730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.12553","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":"2026-05-18T03:10:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ILlebCaEM+rsNeMNAAnp2asfSkO5h1wsZjU26p3zZF5T/XTXQv/PFGDcDy7+VaMCS72HR2G0cpOhjPhJBhQbDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T18:20:46.360589Z"},"content_sha256":"eab34109e9489181cf5bda23ffaeef2f55ff7fee1fa74b8b50c28e4bcdd992e6","schema_version":"1.0","event_id":"sha256:eab34109e9489181cf5bda23ffaeef2f55ff7fee1fa74b8b50c28e4bcdd992e6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:DUUODIEZCIUEGTXOCY3O3FZQAD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ChannelKAN: Multi-Scale Dual-Domain Channel Prediction via Hybrid CNN-KAN Architecture","license":"http://creativecommons.org/licenses/by/4.0/","headline":"ChannelKAN uses a hybrid CNN-KAN architecture to predict channel state information more accurately than RNN, LSTM, GRU, CNN or Transformer models in high-mobility wireless systems.","cross_cats":["cs.AI"],"primary_cat":"eess.SP","authors_text":"Nanqing Jiang, Tao Guo, Xiaoyu Zhao, Yinfei Xu, Zhangyao Song","submitted_at":"2026-05-11T07:58:51Z","abstract_excerpt":"Accurate channel state information (CSI) prediction is essential for improving the reliability and spectral efficiency of massive MIMO-OFDM systems in high-mobility scenarios. Existing deep learning methods struggle to jointly capture short-term local variations and long-range nonlinear dependencies in CSI sequences. To address this challenge, we propose ChannelKAN, a hybrid CNN-KAN channel prediction model with multi-scale frequency domain information enhancement. The key insight is that CNNs and Kolmogorov-Arnold Networks (KANs) are naturally complementary: CNNs extract intra-time-step local"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experiments on 3GPP-compliant QuaDRiGa datasets demonstrate that ChannelKAN outperforms RNN, LSTM, GRU, CNN, and Transformer baselines in normalized mean square error (NMSE), spectral efficiency (SE), and bit error rate (BER) across various velocities and signal-to-noise ratios.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That performance measured on QuaDRiGa ray-tracing simulations will translate to real-world measured channels without retraining or domain adaptation.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A hybrid CNN-KAN model with dual-domain and multi-scale frequency enhancement predicts CSI more accurately than RNN, LSTM, GRU, CNN, and Transformer baselines on QuaDRiGa simulations.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"ChannelKAN uses a hybrid CNN-KAN architecture to predict channel state information more accurately than RNN, LSTM, GRU, CNN or Transformer models in high-mobility wireless systems.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"033116c011b6a0014763a3d47bec0e8be4539b93e811e297e7c58483c0f01f5f"},"source":{"id":"2605.12553","kind":"arxiv","version":1},"verdict":{"id":"a85b0eaa-6293-407b-8e3a-910614099087","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T21:56:10.453550Z","strongest_claim":"Experiments on 3GPP-compliant QuaDRiGa datasets demonstrate that ChannelKAN outperforms RNN, LSTM, GRU, CNN, and Transformer baselines in normalized mean square error (NMSE), spectral efficiency (SE), and bit error rate (BER) across various velocities and signal-to-noise ratios.","one_line_summary":"A hybrid CNN-KAN model with dual-domain and multi-scale frequency enhancement predicts CSI more accurately than RNN, LSTM, GRU, CNN, and Transformer baselines on QuaDRiGa simulations.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That performance measured on QuaDRiGa ray-tracing simulations will translate to real-world measured channels without retraining or domain adaptation.","pith_extraction_headline":"ChannelKAN uses a hybrid CNN-KAN architecture to predict channel state information more accurately than RNN, LSTM, GRU, CNN or Transformer models in high-mobility wireless systems."},"references":{"count":21,"sample":[{"doi":"","year":2019,"title":"Neural network-based fading channel pre- diction: A comprehensive overview,","work_id":"61c8eac5-9ad1-4a75-9cae-78adc5f660cc","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"End-to-end deep learning for tdd mimo systems in the 6g upper midbands,","work_id":"05f54436-443e-4af7-8260-7604b473832f","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Addressing the curse of mobility in massive mimo with prony-based angular-delay domain chan- nel predictions,","work_id":"1375a552-6bf6-497e-97b3-1c10e1bb15df","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Massive mimo channel prediction: Kalman filtering vs. machine learning,","work_id":"59e63f16-0b08-407d-842b-e9b4b3cb37fb","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Model enhanced learning based detectors (me-lead) for wideband multi-user 1-bit mmwave com- munications,","work_id":"feed4c85-3fc7-4b0e-8c6b-6fe0f65d9dd5","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":21,"snapshot_sha256":"2664c4f32390d25ffcb58e053bde5da031f063ba249e7128112a02bc7a8a5c4b","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"94c0b2a98c420f39962379b4f8277b9784864623c5f19b55d2f24a0198971a5f"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"a85b0eaa-6293-407b-8e3a-910614099087"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:10:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2fA/Z8YxYMNTSHKeefMIjFamsjEpB4+mlupCtAg7ELhvyD7NFFzhWat4Rl8Jq2scLwKeLeS7CRMtAyVV8Mq5DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T18:20:46.361691Z"},"content_sha256":"174d90e3d8f1ecf06e487612792d44169ed92169c996b1868ace0f270352d982","schema_version":"1.0","event_id":"sha256:174d90e3d8f1ecf06e487612792d44169ed92169c996b1868ace0f270352d982"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DUUODIEZCIUEGTXOCY3O3FZQAD/bundle.json","state_url":"https://pith.science/pith/DUUODIEZCIUEGTXOCY3O3FZQAD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DUUODIEZCIUEGTXOCY3O3FZQAD/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":"2026-05-25T18:20:46Z","links":{"resolver":"https://pith.science/pith/DUUODIEZCIUEGTXOCY3O3FZQAD","bundle":"https://pith.science/pith/DUUODIEZCIUEGTXOCY3O3FZQAD/bundle.json","state":"https://pith.science/pith/DUUODIEZCIUEGTXOCY3O3FZQAD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DUUODIEZCIUEGTXOCY3O3FZQAD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:DUUODIEZCIUEGTXOCY3O3FZQAD","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":"e6b2d9a67c83a62e245be874c87f9cf5a19a5e8901a43ed9dd2d2e3655af8e68","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-11T07:58:51Z","title_canon_sha256":"91f834b227a7d9d06583b81c501031b01223c077a03749b51ca3b1382e860488"},"schema_version":"1.0","source":{"id":"2605.12553","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12553","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12553v1","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12553","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"pith_short_12","alias_value":"DUUODIEZCIUE","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"DUUODIEZCIUEGTXO","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"DUUODIEZ","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:174d90e3d8f1ecf06e487612792d44169ed92169c996b1868ace0f270352d982","target":"graph","created_at":"2026-05-18T03:10:02Z","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":"Experiments on 3GPP-compliant QuaDRiGa datasets demonstrate that ChannelKAN outperforms RNN, LSTM, GRU, CNN, and Transformer baselines in normalized mean square error (NMSE), spectral efficiency (SE), and bit error rate (BER) across various velocities and signal-to-noise ratios."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That performance measured on QuaDRiGa ray-tracing simulations will translate to real-world measured channels without retraining or domain adaptation."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A hybrid CNN-KAN model with dual-domain and multi-scale frequency enhancement predicts CSI more accurately than RNN, LSTM, GRU, CNN, and Transformer baselines on QuaDRiGa simulations."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"ChannelKAN uses a hybrid CNN-KAN architecture to predict channel state information more accurately than RNN, LSTM, GRU, CNN or Transformer models in high-mobility wireless systems."}],"snapshot_sha256":"033116c011b6a0014763a3d47bec0e8be4539b93e811e297e7c58483c0f01f5f"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"94c0b2a98c420f39962379b4f8277b9784864623c5f19b55d2f24a0198971a5f"},"paper":{"abstract_excerpt":"Accurate channel state information (CSI) prediction is essential for improving the reliability and spectral efficiency of massive MIMO-OFDM systems in high-mobility scenarios. Existing deep learning methods struggle to jointly capture short-term local variations and long-range nonlinear dependencies in CSI sequences. To address this challenge, we propose ChannelKAN, a hybrid CNN-KAN channel prediction model with multi-scale frequency domain information enhancement. The key insight is that CNNs and Kolmogorov-Arnold Networks (KANs) are naturally complementary: CNNs extract intra-time-step local","authors_text":"Nanqing Jiang, Tao Guo, Xiaoyu Zhao, Yinfei Xu, Zhangyao Song","cross_cats":["cs.AI"],"headline":"ChannelKAN uses a hybrid CNN-KAN architecture to predict channel state information more accurately than RNN, LSTM, GRU, CNN or Transformer models in high-mobility wireless systems.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-11T07:58:51Z","title":"ChannelKAN: Multi-Scale Dual-Domain Channel Prediction via Hybrid CNN-KAN Architecture"},"references":{"count":21,"internal_anchors":0,"resolved_work":21,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Neural network-based fading channel pre- diction: A comprehensive overview,","work_id":"61c8eac5-9ad1-4a75-9cae-78adc5f660cc","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"End-to-end deep learning for tdd mimo systems in the 6g upper midbands,","work_id":"05f54436-443e-4af7-8260-7604b473832f","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Addressing the curse of mobility in massive mimo with prony-based angular-delay domain chan- nel predictions,","work_id":"1375a552-6bf6-497e-97b3-1c10e1bb15df","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Massive mimo channel prediction: Kalman filtering vs. machine learning,","work_id":"59e63f16-0b08-407d-842b-e9b4b3cb37fb","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Model enhanced learning based detectors (me-lead) for wideband multi-user 1-bit mmwave com- munications,","work_id":"feed4c85-3fc7-4b0e-8c6b-6fe0f65d9dd5","year":2021}],"snapshot_sha256":"2664c4f32390d25ffcb58e053bde5da031f063ba249e7128112a02bc7a8a5c4b"},"source":{"id":"2605.12553","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T21:56:10.453550Z","id":"a85b0eaa-6293-407b-8e3a-910614099087","model_set":{"reader":"grok-4.3"},"one_line_summary":"A hybrid CNN-KAN model with dual-domain and multi-scale frequency enhancement predicts CSI more accurately than RNN, LSTM, GRU, CNN, and Transformer baselines on QuaDRiGa simulations.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"ChannelKAN uses a hybrid CNN-KAN architecture to predict channel state information more accurately than RNN, LSTM, GRU, CNN or Transformer models in high-mobility wireless systems.","strongest_claim":"Experiments on 3GPP-compliant QuaDRiGa datasets demonstrate that ChannelKAN outperforms RNN, LSTM, GRU, CNN, and Transformer baselines in normalized mean square error (NMSE), spectral efficiency (SE), and bit error rate (BER) across various velocities and signal-to-noise ratios.","weakest_assumption":"That performance measured on QuaDRiGa ray-tracing simulations will translate to real-world measured channels without retraining or domain adaptation."}},"verdict_id":"a85b0eaa-6293-407b-8e3a-910614099087"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:eab34109e9489181cf5bda23ffaeef2f55ff7fee1fa74b8b50c28e4bcdd992e6","target":"record","created_at":"2026-05-18T03:10:02Z","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":"e6b2d9a67c83a62e245be874c87f9cf5a19a5e8901a43ed9dd2d2e3655af8e68","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-11T07:58:51Z","title_canon_sha256":"91f834b227a7d9d06583b81c501031b01223c077a03749b51ca3b1382e860488"},"schema_version":"1.0","source":{"id":"2605.12553","kind":"arxiv","version":1}},"canonical_sha256":"1d28e1a0991228434eee1636ed973000fd0d5aeeb82fd236a129edd7f753c69f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1d28e1a0991228434eee1636ed973000fd0d5aeeb82fd236a129edd7f753c69f","first_computed_at":"2026-05-18T03:10:02.097730Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:10:02.097730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EPnUbgc7ofLhar/uKOFmxGHrdWrYnFWeaRyaigr1qi30zeF1qG8UcAmtugz7lxDG2Gxpe5tiR1nKJPi7CLJNDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:10:02.098260Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12553","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eab34109e9489181cf5bda23ffaeef2f55ff7fee1fa74b8b50c28e4bcdd992e6","sha256:174d90e3d8f1ecf06e487612792d44169ed92169c996b1868ace0f270352d982"],"state_sha256":"e420b7e9f6bdde6edeb6a70e8556d850af1d250f65697ff6531e28b0d22a8884"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z7r0OUqawuFx3Vp5BaXt/LegKJl6f6hJfPBDNV/uwJY5xuWOXcpvj75p43srYerDK3LGCbGVElPDEiavV2NPDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T18:20:46.366276Z","bundle_sha256":"e2fe97c4c4199fefaab9fcfbd2739d2260771adeeed622c4a79e413874989ffc"}}