{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:XGHV5MA2AGK7S56AY6WGANHN46","short_pith_number":"pith:XGHV5MA2","canonical_record":{"source":{"id":"2605.13621","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T14:51:05Z","cross_cats_sorted":[],"title_canon_sha256":"f49fed222480a2be2aec22a7f9fc42fefd6c2b4e8fb2c75361670da6e9fe1114","abstract_canon_sha256":"541a5fea8077e25a13fbff74942905a286ea5c5806fc4cd5f0e17ea2c34a5835"},"schema_version":"1.0"},"canonical_sha256":"b98f5eb01a0195f977c0c7ac6034ede7bd3b86046d89ce27824c428b3f7eb12c","source":{"kind":"arxiv","id":"2605.13621","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13621","created_at":"2026-05-18T02:44:17Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13621v1","created_at":"2026-05-18T02:44:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13621","created_at":"2026-05-18T02:44:17Z"},{"alias_kind":"pith_short_12","alias_value":"XGHV5MA2AGK7","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"XGHV5MA2AGK7S56A","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"XGHV5MA2","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:XGHV5MA2AGK7S56AY6WGANHN46","target":"record","payload":{"canonical_record":{"source":{"id":"2605.13621","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T14:51:05Z","cross_cats_sorted":[],"title_canon_sha256":"f49fed222480a2be2aec22a7f9fc42fefd6c2b4e8fb2c75361670da6e9fe1114","abstract_canon_sha256":"541a5fea8077e25a13fbff74942905a286ea5c5806fc4cd5f0e17ea2c34a5835"},"schema_version":"1.0"},"canonical_sha256":"b98f5eb01a0195f977c0c7ac6034ede7bd3b86046d89ce27824c428b3f7eb12c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:17.878061Z","signature_b64":"ngvbyN2Ao8Tfca1AWPopie0JFQKegZ+ij0SQTYM+tPKx2F5vq1R5fl5CLgZGWcD0HoXGae/9CtVowdCv9J5kAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b98f5eb01a0195f977c0c7ac6034ede7bd3b86046d89ce27824c428b3f7eb12c","last_reissued_at":"2026-05-18T02:44:17.877587Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:17.877587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.13621","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":"2026-05-18T02:44:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DDjXGVKE6CX3ylJRFbpRSwqwP0Q/9++VjSd24NpZJ6lOHl4smbAnWPr/0HDqRjJa4SKVGk+NwzTH+CxpG/NDCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T07:33:17.659594Z"},"content_sha256":"2a4f1e0c3fbd0b56f2209aa5128ba8d0badc3a8f5eda5eea13f27c1e77ef7c62","schema_version":"1.0","event_id":"sha256:2a4f1e0c3fbd0b56f2209aa5128ba8d0badc3a8f5eda5eea13f27c1e77ef7c62"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:XGHV5MA2AGK7S56AY6WGANHN46","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"WD-FQDet: Multispectral Detection Transformer via Wavelet Decomposition and Frequency-aware Query Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Wavelet decomposition decouples shared low-frequency and specific high-frequency features from infrared and visible images to improve object detection.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunjin Yang, Fanman Meng, Xiwei Zhang, Yiming Xiao","submitted_at":"2026-05-13T14:51:05Z","abstract_excerpt":"Infrared-visible object detection improves detection performance by combining complementary features from multispectral images. Existing backbone-specific and backbone-shared approaches still suffer from the problems of severe bias of modality-shared features and the insufficiency of modality-specific features. To address these issues, we propose a novel detection framework WD-FQDet that explicitly decouples modality-shared and modality-specific information from infrared and visible modalities in the new view of low- and high-frequency domains, allowing fusion strategies tailored to their freq"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"we propose a novel detection framework WD-FQDet that explicitly decouples modality-shared and modality-specific information from infrared and visible modalities in the new view of low- and high-frequency domains, allowing fusion strategies tailored to their frequency characteristics... Experimental results on the FLIR, LLVIP, and M3FD datasets demonstrate that WD-FQDet achieves state-of-the-art performance across multiple evaluation metrics.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that wavelet decomposition cleanly separates modality-shared low-frequency features from modality-specific high-frequency features and that the proposed alignment, retention, and query modules will mitigate bias and insufficiency without introducing artifacts or overfitting to the specific datasets.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"WD-FQDet decouples modality-shared and modality-specific features in infrared-visible images via wavelet-based frequency decomposition and frequency-aware query selection to achieve state-of-the-art detection performance.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Wavelet decomposition decouples shared low-frequency and specific high-frequency features from infrared and visible images to improve object detection.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"87fc3f1c0b11d0262d0fd70ebd1f21532de95a867326053d9e6009941ef316bb"},"source":{"id":"2605.13621","kind":"arxiv","version":1},"verdict":{"id":"34f44e37-e1d5-4cf8-aa8e-7cf1182cf642","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T19:11:48.767464Z","strongest_claim":"we propose a novel detection framework WD-FQDet that explicitly decouples modality-shared and modality-specific information from infrared and visible modalities in the new view of low- and high-frequency domains, allowing fusion strategies tailored to their frequency characteristics... Experimental results on the FLIR, LLVIP, and M3FD datasets demonstrate that WD-FQDet achieves state-of-the-art performance across multiple evaluation metrics.","one_line_summary":"WD-FQDet decouples modality-shared and modality-specific features in infrared-visible images via wavelet-based frequency decomposition and frequency-aware query selection to achieve state-of-the-art detection performance.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that wavelet decomposition cleanly separates modality-shared low-frequency features from modality-specific high-frequency features and that the proposed alignment, retention, and query modules will mitigate bias and insufficiency without introducing artifacts or overfitting to the specific datasets.","pith_extraction_headline":"Wavelet decomposition decouples shared low-frequency and specific high-frequency features from infrared and visible images to improve object detection."},"references":{"count":63,"sample":[{"doi":"","year":2015,"title":"Multi-modality medical im- age fusion using discrete wavelet transform.Procedia Com- puter Science, 70:625–631, 2015","work_id":"930c14ef-2f69-426c-b7fc-48a3bdcef75a","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Multimodal object detection by channel switching and spatial attention","work_id":"4f218d8f-05e3-4b0e-83fc-8d0b2cd01c5c","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"End-to- end object detection with transformers","work_id":"e5f6fe5f-1010-46d2-b656-b3f7803d7225","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Timothy Chase Jr, Chris Gnam, John Crassidis, and Karthik Dantu. You only crash once: Improved object detection for real-time, sim-to-real hazardous terrain detection and classi- fication for autonomo","work_id":"2d4b14db-8329-4eea-83f4-e6a240aa0e2b","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Multimodal object detection via probabilistic ensembling","work_id":"0a65ddb6-235a-4b26-af23-53ac568ad7d6","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":63,"snapshot_sha256":"9dcaa972721fcf8b4f0d590f6bd7884752b172caf01805cbcfe8f242634bec0e","internal_anchors":2},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"34f44e37-e1d5-4cf8-aa8e-7cf1182cf642"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:44:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CJHo37NzWqbuwcmUHwKtcWEI1JzYX5vnh9kXp+YzvVk6Y1J1aCNc2XHm53M8YLzQYnJ3LFmsb+MY5GixhP78BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T07:33:17.660235Z"},"content_sha256":"460b2caf94e1f054e3293351c296f874f1371f7d124f7c5d3af5637965f8b748","schema_version":"1.0","event_id":"sha256:460b2caf94e1f054e3293351c296f874f1371f7d124f7c5d3af5637965f8b748"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XGHV5MA2AGK7S56AY6WGANHN46/bundle.json","state_url":"https://pith.science/pith/XGHV5MA2AGK7S56AY6WGANHN46/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XGHV5MA2AGK7S56AY6WGANHN46/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":"2026-05-28T07:33:17Z","links":{"resolver":"https://pith.science/pith/XGHV5MA2AGK7S56AY6WGANHN46","bundle":"https://pith.science/pith/XGHV5MA2AGK7S56AY6WGANHN46/bundle.json","state":"https://pith.science/pith/XGHV5MA2AGK7S56AY6WGANHN46/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XGHV5MA2AGK7S56AY6WGANHN46/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:XGHV5MA2AGK7S56AY6WGANHN46","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":"541a5fea8077e25a13fbff74942905a286ea5c5806fc4cd5f0e17ea2c34a5835","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T14:51:05Z","title_canon_sha256":"f49fed222480a2be2aec22a7f9fc42fefd6c2b4e8fb2c75361670da6e9fe1114"},"schema_version":"1.0","source":{"id":"2605.13621","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13621","created_at":"2026-05-18T02:44:17Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13621v1","created_at":"2026-05-18T02:44:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13621","created_at":"2026-05-18T02:44:17Z"},{"alias_kind":"pith_short_12","alias_value":"XGHV5MA2AGK7","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"XGHV5MA2AGK7S56A","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"XGHV5MA2","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:460b2caf94e1f054e3293351c296f874f1371f7d124f7c5d3af5637965f8b748","target":"graph","created_at":"2026-05-18T02:44:17Z","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 a novel detection framework WD-FQDet that explicitly decouples modality-shared and modality-specific information from infrared and visible modalities in the new view of low- and high-frequency domains, allowing fusion strategies tailored to their frequency characteristics... Experimental results on the FLIR, LLVIP, and M3FD datasets demonstrate that WD-FQDet achieves state-of-the-art performance across multiple evaluation metrics."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that wavelet decomposition cleanly separates modality-shared low-frequency features from modality-specific high-frequency features and that the proposed alignment, retention, and query modules will mitigate bias and insufficiency without introducing artifacts or overfitting to the specific datasets."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"WD-FQDet decouples modality-shared and modality-specific features in infrared-visible images via wavelet-based frequency decomposition and frequency-aware query selection to achieve state-of-the-art detection performance."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Wavelet decomposition decouples shared low-frequency and specific high-frequency features from infrared and visible images to improve object detection."}],"snapshot_sha256":"87fc3f1c0b11d0262d0fd70ebd1f21532de95a867326053d9e6009941ef316bb"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Infrared-visible object detection improves detection performance by combining complementary features from multispectral images. Existing backbone-specific and backbone-shared approaches still suffer from the problems of severe bias of modality-shared features and the insufficiency of modality-specific features. To address these issues, we propose a novel detection framework WD-FQDet that explicitly decouples modality-shared and modality-specific information from infrared and visible modalities in the new view of low- and high-frequency domains, allowing fusion strategies tailored to their freq","authors_text":"Chunjin Yang, Fanman Meng, Xiwei Zhang, Yiming Xiao","cross_cats":[],"headline":"Wavelet decomposition decouples shared low-frequency and specific high-frequency features from infrared and visible images to improve object detection.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T14:51:05Z","title":"WD-FQDet: Multispectral Detection Transformer via Wavelet Decomposition and Frequency-aware Query Learning"},"references":{"count":63,"internal_anchors":2,"resolved_work":63,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Multi-modality medical im- age fusion using discrete wavelet transform.Procedia Com- puter Science, 70:625–631, 2015","work_id":"930c14ef-2f69-426c-b7fc-48a3bdcef75a","year":2015},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Multimodal object detection by channel switching and spatial attention","work_id":"4f218d8f-05e3-4b0e-83fc-8d0b2cd01c5c","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"End-to- end object detection with transformers","work_id":"e5f6fe5f-1010-46d2-b656-b3f7803d7225","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Timothy Chase Jr, Chris Gnam, John Crassidis, and Karthik Dantu. You only crash once: Improved object detection for real-time, sim-to-real hazardous terrain detection and classi- fication for autonomo","work_id":"2d4b14db-8329-4eea-83f4-e6a240aa0e2b","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Multimodal object detection via probabilistic ensembling","work_id":"0a65ddb6-235a-4b26-af23-53ac568ad7d6","year":2022}],"snapshot_sha256":"9dcaa972721fcf8b4f0d590f6bd7884752b172caf01805cbcfe8f242634bec0e"},"source":{"id":"2605.13621","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T19:11:48.767464Z","id":"34f44e37-e1d5-4cf8-aa8e-7cf1182cf642","model_set":{"reader":"grok-4.3"},"one_line_summary":"WD-FQDet decouples modality-shared and modality-specific features in infrared-visible images via wavelet-based frequency decomposition and frequency-aware query selection to achieve state-of-the-art detection performance.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Wavelet decomposition decouples shared low-frequency and specific high-frequency features from infrared and visible images to improve object detection.","strongest_claim":"we propose a novel detection framework WD-FQDet that explicitly decouples modality-shared and modality-specific information from infrared and visible modalities in the new view of low- and high-frequency domains, allowing fusion strategies tailored to their frequency characteristics... Experimental results on the FLIR, LLVIP, and M3FD datasets demonstrate that WD-FQDet achieves state-of-the-art performance across multiple evaluation metrics.","weakest_assumption":"The assumption that wavelet decomposition cleanly separates modality-shared low-frequency features from modality-specific high-frequency features and that the proposed alignment, retention, and query modules will mitigate bias and insufficiency without introducing artifacts or overfitting to the specific datasets."}},"verdict_id":"34f44e37-e1d5-4cf8-aa8e-7cf1182cf642"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2a4f1e0c3fbd0b56f2209aa5128ba8d0badc3a8f5eda5eea13f27c1e77ef7c62","target":"record","created_at":"2026-05-18T02:44:17Z","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":"541a5fea8077e25a13fbff74942905a286ea5c5806fc4cd5f0e17ea2c34a5835","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T14:51:05Z","title_canon_sha256":"f49fed222480a2be2aec22a7f9fc42fefd6c2b4e8fb2c75361670da6e9fe1114"},"schema_version":"1.0","source":{"id":"2605.13621","kind":"arxiv","version":1}},"canonical_sha256":"b98f5eb01a0195f977c0c7ac6034ede7bd3b86046d89ce27824c428b3f7eb12c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b98f5eb01a0195f977c0c7ac6034ede7bd3b86046d89ce27824c428b3f7eb12c","first_computed_at":"2026-05-18T02:44:17.877587Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:44:17.877587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ngvbyN2Ao8Tfca1AWPopie0JFQKegZ+ij0SQTYM+tPKx2F5vq1R5fl5CLgZGWcD0HoXGae/9CtVowdCv9J5kAg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:44:17.878061Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.13621","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2a4f1e0c3fbd0b56f2209aa5128ba8d0badc3a8f5eda5eea13f27c1e77ef7c62","sha256:460b2caf94e1f054e3293351c296f874f1371f7d124f7c5d3af5637965f8b748"],"state_sha256":"8532ecfc509bcc7a89edaf5b282d9fb46d9407ff2eb4f170b66c67e2074067e2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N4rv3I9/vON9ASau9W9DB3N5o+7J/zTpbpkxsHNpcCm7D5c2gJh10+tR8Eckkl3H25ue4ZlOYltUkvDedlhCBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T07:33:17.662915Z","bundle_sha256":"b216eeaf2a95ee4be866e7b07bceac4731dbbad5f94518b6e43ba966297e44c3"}}