{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:BJX7SPCDNEXURBQ54QJKNSAGBP","short_pith_number":"pith:BJX7SPCD","canonical_record":{"source":{"id":"2605.12562","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-05-12T03:40:38Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"f2f3f811642975b559580f6755c326bced224e70629501bbe996b9dfe04bf33e","abstract_canon_sha256":"2099a75263083ba9f2a7c93c8bcf3592b4f47542cfc1677772723c01a7bdf489"},"schema_version":"1.0"},"canonical_sha256":"0a6ff93c43692f48861de412a6c8060bc11696f6aa1607d934fbd512338b9686","source":{"kind":"arxiv","id":"2605.12562","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12562","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12562v1","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12562","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"pith_short_12","alias_value":"BJX7SPCDNEXU","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"BJX7SPCDNEXURBQ5","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"BJX7SPCD","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:BJX7SPCDNEXURBQ54QJKNSAGBP","target":"record","payload":{"canonical_record":{"source":{"id":"2605.12562","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-05-12T03:40:38Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"f2f3f811642975b559580f6755c326bced224e70629501bbe996b9dfe04bf33e","abstract_canon_sha256":"2099a75263083ba9f2a7c93c8bcf3592b4f47542cfc1677772723c01a7bdf489"},"schema_version":"1.0"},"canonical_sha256":"0a6ff93c43692f48861de412a6c8060bc11696f6aa1607d934fbd512338b9686","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:10:01.962589Z","signature_b64":"ikUWvq6zrmaGnlJLScW5aMmqncDnWut/HRe7XbTjBkA58zVOUWCu1MRlTxMojBU4vgVGKQVop15Oolu76SldDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a6ff93c43692f48861de412a6c8060bc11696f6aa1607d934fbd512338b9686","last_reissued_at":"2026-05-18T03:10:01.961694Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:10:01.961694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.12562","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":"2026-05-18T03:10:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PeE08YSb4saq9h3yNxtaHsqKGq1ga6snutIgmLCjO2EXjeaJM2sSHwTNrxlYh6pMbngIL3tQcIdTmSV/3dDvAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T13:30:44.715258Z"},"content_sha256":"1bbd58312487ceec0dcd1aa678156fabebce2483a6ecc6d497b54842fba1c41f","schema_version":"1.0","event_id":"sha256:1bbd58312487ceec0dcd1aa678156fabebce2483a6ecc6d497b54842fba1c41f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:BJX7SPCDNEXURBQ54QJKNSAGBP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Distilling knowledge from the best CT window transfers latent pathological signatures to students on other windows and raises per-window AUC by 10-16 points.","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Bo Peng, Daqian Shi, Honghan Wu, Jing Gao, Johan Thygesen, Kun Wang, Na Wang, Tian Li, Wujian Xu, Ximing Liao, Yingqun Ji","submitted_at":"2026-05-12T03:40:38Z","abstract_excerpt":"Multi-window CT imaging captures complementary pathological information across anatomical structures of differing densities, yet existing deep learning methods fuse representations only at later stages, missing cross-density interactions. We propose a cross-window knowledge distillation framework in which student encoders learn latent clinical priors from a teacher trained on the most informative window. Evaluated retrospectively on three cohorts - COPD-CT-DF (n=719), RSNA PE (n=1,433), and an in-house CTEPD dataset (n=161) - distillation improved per-window AUC by 10.1-16.5 percentage points "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"distillation improved per-window AUC by 10.1-16.5 percentage points on COPD-CT-DF (0.75-0.81 to 0.90-0.94; all P<0.001), with ensemble AUC reaching 0.9960. Similar gains were observed on RSNA PE (0.80-0.83 to 0.90-0.92) and CTEPD (AUC 0.7481 vs. 0.6264).","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the teacher model trained on the single most informative window captures all clinically relevant cross-density pathological signatures that can be successfully distilled to student encoders on other windows without introducing bias or loss of information specific to those windows.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Cross-window knowledge distillation raises per-window AUC by 10-16 points in pulmonary CT by transferring latent pathological signatures from a teacher encoder on the most informative window to students on other windows.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Distilling knowledge from the best CT window transfers latent pathological signatures to students on other windows and raises per-window AUC by 10-16 points.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f1e1547afae2bd6594e8a57ff8ba7c750c0ee9ab0c167fa3512dcdd35289883d"},"source":{"id":"2605.12562","kind":"arxiv","version":1},"verdict":{"id":"1e7f06f6-1c7a-4154-8011-889deec95f11","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T20:50:13.032150Z","strongest_claim":"distillation improved per-window AUC by 10.1-16.5 percentage points on COPD-CT-DF (0.75-0.81 to 0.90-0.94; all P<0.001), with ensemble AUC reaching 0.9960. Similar gains were observed on RSNA PE (0.80-0.83 to 0.90-0.92) and CTEPD (AUC 0.7481 vs. 0.6264).","one_line_summary":"Cross-window knowledge distillation raises per-window AUC by 10-16 points in pulmonary CT by transferring latent pathological signatures from a teacher encoder on the most informative window to students on other windows.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the teacher model trained on the single most informative window captures all clinically relevant cross-density pathological signatures that can be successfully distilled to student encoders on other windows without introducing bias or loss of information specific to those windows.","pith_extraction_headline":"Distilling knowledge from the best CT window transfers latent pathological signatures to students on other windows and raises per-window AUC by 10-16 points."},"references":{"count":22,"sample":[{"doi":"10.1186/s12931-024-02913-z","year":2024,"title":"Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respir Res, 25(1):319, 2024","work_id":"ef27bcd5-42f1-4997-b30c-5c7725a6a783","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2015,"title":"Fundamentals of Radiology","work_id":"6279b252-480f-4c7a-99a4-29370f481a25","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"The CT pulmonary vascular parameters and disease severity in COPD patients on acute exacerbation: a correlation analysis.BMC Pulm Med, 21(1):34, 2021","work_id":"efa2f0fc-ef85-425c-ab5f-853b8a31d3a0","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2015,"title":"Bartolome R Celli, Marc Decramer, Jadwiga A Wedzicha, Kevin C Wilson, Alvar Agust´ ı, Gerard J Criner, et al. An official American Thoracic Society/European Respiratory Society statement: research que","work_id":"c8aa9c4f-8eab-4d0d-bcca-969621034667","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis.NPJ Digit Med, 8(1):254, 2025","work_id":"ed849861-128c-4825-9649-e771d06f7cca","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":22,"snapshot_sha256":"f17ae9c9186620bd84a90b49e448480a386ac50a1ce303cfc2a03b2fa17b69df","internal_anchors":0},"formal_canon":{"evidence_count":1,"snapshot_sha256":"72deb389eb47e3227addd03deab41eb927dfb29e5997ee005e559990f2d1d45a"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"1e7f06f6-1c7a-4154-8011-889deec95f11"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:10:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xUE/UOd/n6b6ufDtIvpud3fppajXvhezrJlecSL+CSiVvGEgXaIpL2YlY9JV3hKYqcMqdGzLf8dFyeEyyO8jDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T13:30:44.716433Z"},"content_sha256":"5016da05511e2c49c10e7c984a32ffc3936aea3e4a933d3faf63e310ad751204","schema_version":"1.0","event_id":"sha256:5016da05511e2c49c10e7c984a32ffc3936aea3e4a933d3faf63e310ad751204"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BJX7SPCDNEXURBQ54QJKNSAGBP/bundle.json","state_url":"https://pith.science/pith/BJX7SPCDNEXURBQ54QJKNSAGBP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BJX7SPCDNEXURBQ54QJKNSAGBP/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":"2026-05-26T13:30:44Z","links":{"resolver":"https://pith.science/pith/BJX7SPCDNEXURBQ54QJKNSAGBP","bundle":"https://pith.science/pith/BJX7SPCDNEXURBQ54QJKNSAGBP/bundle.json","state":"https://pith.science/pith/BJX7SPCDNEXURBQ54QJKNSAGBP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BJX7SPCDNEXURBQ54QJKNSAGBP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:BJX7SPCDNEXURBQ54QJKNSAGBP","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":"2099a75263083ba9f2a7c93c8bcf3592b4f47542cfc1677772723c01a7bdf489","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-05-12T03:40:38Z","title_canon_sha256":"f2f3f811642975b559580f6755c326bced224e70629501bbe996b9dfe04bf33e"},"schema_version":"1.0","source":{"id":"2605.12562","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12562","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12562v1","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12562","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"pith_short_12","alias_value":"BJX7SPCDNEXU","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"BJX7SPCDNEXURBQ5","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"BJX7SPCD","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:5016da05511e2c49c10e7c984a32ffc3936aea3e4a933d3faf63e310ad751204","target":"graph","created_at":"2026-05-18T03:10:01Z","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":"distillation improved per-window AUC by 10.1-16.5 percentage points on COPD-CT-DF (0.75-0.81 to 0.90-0.94; all P<0.001), with ensemble AUC reaching 0.9960. Similar gains were observed on RSNA PE (0.80-0.83 to 0.90-0.92) and CTEPD (AUC 0.7481 vs. 0.6264)."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the teacher model trained on the single most informative window captures all clinically relevant cross-density pathological signatures that can be successfully distilled to student encoders on other windows without introducing bias or loss of information specific to those windows."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Cross-window knowledge distillation raises per-window AUC by 10-16 points in pulmonary CT by transferring latent pathological signatures from a teacher encoder on the most informative window to students on other windows."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Distilling knowledge from the best CT window transfers latent pathological signatures to students on other windows and raises per-window AUC by 10-16 points."}],"snapshot_sha256":"f1e1547afae2bd6594e8a57ff8ba7c750c0ee9ab0c167fa3512dcdd35289883d"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"72deb389eb47e3227addd03deab41eb927dfb29e5997ee005e559990f2d1d45a"},"paper":{"abstract_excerpt":"Multi-window CT imaging captures complementary pathological information across anatomical structures of differing densities, yet existing deep learning methods fuse representations only at later stages, missing cross-density interactions. We propose a cross-window knowledge distillation framework in which student encoders learn latent clinical priors from a teacher trained on the most informative window. Evaluated retrospectively on three cohorts - COPD-CT-DF (n=719), RSNA PE (n=1,433), and an in-house CTEPD dataset (n=161) - distillation improved per-window AUC by 10.1-16.5 percentage points ","authors_text":"Bo Peng, Daqian Shi, Honghan Wu, Jing Gao, Johan Thygesen, Kun Wang, Na Wang, Tian Li, Wujian Xu, Ximing Liao, Yingqun Ji","cross_cats":["cs.AI","cs.CV"],"headline":"Distilling knowledge from the best CT window transfers latent pathological signatures to students on other windows and raises per-window AUC by 10-16 points.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-05-12T03:40:38Z","title":"Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation"},"references":{"count":22,"internal_anchors":0,"resolved_work":22,"sample":[{"cited_arxiv_id":"","doi":"10.1186/s12931-024-02913-z","is_internal_anchor":false,"ref_index":1,"title":"Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respir Res, 25(1):319, 2024","work_id":"ef27bcd5-42f1-4997-b30c-5c7725a6a783","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Fundamentals of Radiology","work_id":"6279b252-480f-4c7a-99a4-29370f481a25","year":2015},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"The CT pulmonary vascular parameters and disease severity in COPD patients on acute exacerbation: a correlation analysis.BMC Pulm Med, 21(1):34, 2021","work_id":"efa2f0fc-ef85-425c-ab5f-853b8a31d3a0","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Bartolome R Celli, Marc Decramer, Jadwiga A Wedzicha, Kevin C Wilson, Alvar Agust´ ı, Gerard J Criner, et al. An official American Thoracic Society/European Respiratory Society statement: research que","work_id":"c8aa9c4f-8eab-4d0d-bcca-969621034667","year":2015},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis.NPJ Digit Med, 8(1):254, 2025","work_id":"ed849861-128c-4825-9649-e771d06f7cca","year":2025}],"snapshot_sha256":"f17ae9c9186620bd84a90b49e448480a386ac50a1ce303cfc2a03b2fa17b69df"},"source":{"id":"2605.12562","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T20:50:13.032150Z","id":"1e7f06f6-1c7a-4154-8011-889deec95f11","model_set":{"reader":"grok-4.3"},"one_line_summary":"Cross-window knowledge distillation raises per-window AUC by 10-16 points in pulmonary CT by transferring latent pathological signatures from a teacher encoder on the most informative window to students on other windows.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Distilling knowledge from the best CT window transfers latent pathological signatures to students on other windows and raises per-window AUC by 10-16 points.","strongest_claim":"distillation improved per-window AUC by 10.1-16.5 percentage points on COPD-CT-DF (0.75-0.81 to 0.90-0.94; all P<0.001), with ensemble AUC reaching 0.9960. Similar gains were observed on RSNA PE (0.80-0.83 to 0.90-0.92) and CTEPD (AUC 0.7481 vs. 0.6264).","weakest_assumption":"That the teacher model trained on the single most informative window captures all clinically relevant cross-density pathological signatures that can be successfully distilled to student encoders on other windows without introducing bias or loss of information specific to those windows."}},"verdict_id":"1e7f06f6-1c7a-4154-8011-889deec95f11"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1bbd58312487ceec0dcd1aa678156fabebce2483a6ecc6d497b54842fba1c41f","target":"record","created_at":"2026-05-18T03:10:01Z","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":"2099a75263083ba9f2a7c93c8bcf3592b4f47542cfc1677772723c01a7bdf489","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-05-12T03:40:38Z","title_canon_sha256":"f2f3f811642975b559580f6755c326bced224e70629501bbe996b9dfe04bf33e"},"schema_version":"1.0","source":{"id":"2605.12562","kind":"arxiv","version":1}},"canonical_sha256":"0a6ff93c43692f48861de412a6c8060bc11696f6aa1607d934fbd512338b9686","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0a6ff93c43692f48861de412a6c8060bc11696f6aa1607d934fbd512338b9686","first_computed_at":"2026-05-18T03:10:01.961694Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:10:01.961694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ikUWvq6zrmaGnlJLScW5aMmqncDnWut/HRe7XbTjBkA58zVOUWCu1MRlTxMojBU4vgVGKQVop15Oolu76SldDA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:10:01.962589Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12562","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1bbd58312487ceec0dcd1aa678156fabebce2483a6ecc6d497b54842fba1c41f","sha256:5016da05511e2c49c10e7c984a32ffc3936aea3e4a933d3faf63e310ad751204"],"state_sha256":"561814dc64ec318f14673fc8c89ab3b956104dde84ad4db14690fb7f3e0dac47"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E7itVEPfVU12RqvOKKZxPGaDurKphx8FCjx+/O9sLfOg9rG9UXl28qTXmk/MgP0o0b5khbA0iK6gePkQV4tnDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T13:30:44.721159Z","bundle_sha256":"dac7f33554160fc15c4cd5dfde1fc1a711c51bf71bad539a33dd46bf040901ad"}}