{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:UJBTDMCGB4XF4QFO3L73JIIDOF","short_pith_number":"pith:UJBTDMCG","canonical_record":{"source":{"id":"2605.17039","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T15:19:14Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"a1e462e823449e2622158f3d0f312cda22a8399d761a22166e2f5b27e7641de6","abstract_canon_sha256":"7a2680599d8a6f20ca330fa05c61dfa3463e2a4843fe4ae57fa0de3d4180f3ad"},"schema_version":"1.0"},"canonical_sha256":"a24331b0460f2e5e40aedaffb4a10371701bc223425013115f83a45f5db982fe","source":{"kind":"arxiv","id":"2605.17039","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17039","created_at":"2026-05-20T00:03:37Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17039v1","created_at":"2026-05-20T00:03:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17039","created_at":"2026-05-20T00:03:37Z"},{"alias_kind":"pith_short_12","alias_value":"UJBTDMCGB4XF","created_at":"2026-05-20T00:03:37Z"},{"alias_kind":"pith_short_16","alias_value":"UJBTDMCGB4XF4QFO","created_at":"2026-05-20T00:03:37Z"},{"alias_kind":"pith_short_8","alias_value":"UJBTDMCG","created_at":"2026-05-20T00:03:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:UJBTDMCGB4XF4QFO3L73JIIDOF","target":"record","payload":{"canonical_record":{"source":{"id":"2605.17039","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T15:19:14Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"a1e462e823449e2622158f3d0f312cda22a8399d761a22166e2f5b27e7641de6","abstract_canon_sha256":"7a2680599d8a6f20ca330fa05c61dfa3463e2a4843fe4ae57fa0de3d4180f3ad"},"schema_version":"1.0"},"canonical_sha256":"a24331b0460f2e5e40aedaffb4a10371701bc223425013115f83a45f5db982fe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:37.274100Z","signature_b64":"2fmIok1MlUkF9mjYYBQPhvrv6HEfyCRXyeps1W0binZhHVaAnOswxfhK3Px1/TwMgD5j9QJejYOTjaPS7y9oCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a24331b0460f2e5e40aedaffb4a10371701bc223425013115f83a45f5db982fe","last_reissued_at":"2026-05-20T00:03:37.273148Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:37.273148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.17039","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":"2026-05-20T00:03:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gDRoOpLhgzYuPA2SfUJf41zFzonUJGYDDuXdZx7/rC7Rseqk8jj1zIqGoyzkLFx7n4cy/Mo8LvXxZfae4DYeCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T00:27:31.279697Z"},"content_sha256":"7ee48e09e4281734196ebd3c7f3e4a7fba2dc96978d5e906fe43712030ed6e67","schema_version":"1.0","event_id":"sha256:7ee48e09e4281734196ebd3c7f3e4a7fba2dc96978d5e906fe43712030ed6e67"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:UJBTDMCGB4XF4QFO3L73JIIDOF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A federated learning framework fuses solar irradiance data via co-attention to detect photovoltaic generation fraud while keeping household data private across communities.","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Chenghao Huang, Hao Wang, Xiaolu Chen, Yanru Zhang","submitted_at":"2026-05-16T15:19:14Z","abstract_excerpt":"The wide adoption of residential photovoltaic (PV) systems introduces new challenges for generation fraud detection (FD). Unlike traditional electricity theft detection, which focuses on electricity consumption-side behavior, PV generation fraud detection (PVG-FD) is complicated by the inherent intermittency and uncertainty of PV generation. The distributed nature of PV systems poses further challenges for centralized PVG-FD approaches due to scalability and privacy concerns. This paper develops a privacy-preserving distributed PVG-FD framework based on federated learning (FL). In this framewo"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Extensive experiments on a real-world residential PV dataset validate the effectiveness of the developed method and demonstrate that it outperforms state-of-the-art FL methods across various scenarios, with scalability across community sizes and strong robustness to class imbalance.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that discrepancies between reported PV generation and fused solar irradiance/weather data reliably indicate fraud, and that prototype alignment sufficiently mitigates class imbalance without distorting normal generation patterns (implicit in the co-attention and FL aggregation description).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A federated learning framework fuses solar irradiance and PV generation data via co-attention, uses prototype alignment for imbalance, and aggregates models across communities to detect generation fraud while preserving privacy.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A federated learning framework fuses solar irradiance data via co-attention to detect photovoltaic generation fraud while keeping household data private across communities.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3f6e156252b24b4c26143665f74855f1e9e6d2d72f9dec96f2871dacafa831ff"},"source":{"id":"2605.17039","kind":"arxiv","version":1},"verdict":{"id":"2aab4dc2-268e-48b5-9c4e-f8d2f4753841","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:13:04.550723Z","strongest_claim":"Extensive experiments on a real-world residential PV dataset validate the effectiveness of the developed method and demonstrate that it outperforms state-of-the-art FL methods across various scenarios, with scalability across community sizes and strong robustness to class imbalance.","one_line_summary":"A federated learning framework fuses solar irradiance and PV generation data via co-attention, uses prototype alignment for imbalance, and aggregates models across communities to detect generation fraud while preserving privacy.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that discrepancies between reported PV generation and fused solar irradiance/weather data reliably indicate fraud, and that prototype alignment sufficiently mitigates class imbalance without distorting normal generation patterns (implicit in the co-attention and FL aggregation description).","pith_extraction_headline":"A federated learning framework fuses solar irradiance data via co-attention to detect photovoltaic generation fraud while keeping household data private across communities."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.17039/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T20:31:18.921308Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:20:42.557293Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T19:49:41.692432Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T18:51:56.308326Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.160143Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:22.996445Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"a4ad3a8b6b656cc061000691205101e382f0cdde273496636d4400bf4a3c6577"},"references":{"count":44,"sample":[{"doi":"","year":2024,"title":"International Energy Agency, “Renewables 2024,” 2023. [Online]. Available: https://www.iea.org/reports/renewables-2024","work_id":"9d20969d-56a8-48d5-992d-6c18d9adb9bb","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Renewable energy market update,","work_id":"d2b1b7c0-e739-4dc4-8d9d-f257cb3054d3","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"A review of cyber security risks of power systems: from static to dynamic false data attacks,","work_id":"fcf1fba8-831a-405b-b6ea-9adf52f8fbf6","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Detection methods in smart meters for electricity thefts: A survey,","work_id":"e263eaeb-f3b5-4b9d-ab94-88beed82073e","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Deep learning detection of electricity theft cyber-attacks in renewable distributed generation,","work_id":"39468152-171b-4eb0-af24-9ec47e012ebc","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":44,"snapshot_sha256":"c376420fcbdd6c7bd7a431d499b9c705271f605f82d0a917c580b999f3c7c778","internal_anchors":0},"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":"2aab4dc2-268e-48b5-9c4e-f8d2f4753841"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HFM/Kddm3dAbvUWEw1S56RkmmVAvrZLhgA0b+MaQ3k9TYBVaQvp4Xi9V94sHoK/zoRIROtaM6OckfgjyPQ/FCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T00:27:31.281125Z"},"content_sha256":"7d5d5ff4ad729e2011811045731da33bec1ed73276e9b5817d7200fc2c8b2b7d","schema_version":"1.0","event_id":"sha256:7d5d5ff4ad729e2011811045731da33bec1ed73276e9b5817d7200fc2c8b2b7d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UJBTDMCGB4XF4QFO3L73JIIDOF/bundle.json","state_url":"https://pith.science/pith/UJBTDMCGB4XF4QFO3L73JIIDOF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UJBTDMCGB4XF4QFO3L73JIIDOF/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":"2026-05-27T00:27:31Z","links":{"resolver":"https://pith.science/pith/UJBTDMCGB4XF4QFO3L73JIIDOF","bundle":"https://pith.science/pith/UJBTDMCGB4XF4QFO3L73JIIDOF/bundle.json","state":"https://pith.science/pith/UJBTDMCGB4XF4QFO3L73JIIDOF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UJBTDMCGB4XF4QFO3L73JIIDOF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:UJBTDMCGB4XF4QFO3L73JIIDOF","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":"7a2680599d8a6f20ca330fa05c61dfa3463e2a4843fe4ae57fa0de3d4180f3ad","cross_cats_sorted":["cs.CE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T15:19:14Z","title_canon_sha256":"a1e462e823449e2622158f3d0f312cda22a8399d761a22166e2f5b27e7641de6"},"schema_version":"1.0","source":{"id":"2605.17039","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17039","created_at":"2026-05-20T00:03:37Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17039v1","created_at":"2026-05-20T00:03:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17039","created_at":"2026-05-20T00:03:37Z"},{"alias_kind":"pith_short_12","alias_value":"UJBTDMCGB4XF","created_at":"2026-05-20T00:03:37Z"},{"alias_kind":"pith_short_16","alias_value":"UJBTDMCGB4XF4QFO","created_at":"2026-05-20T00:03:37Z"},{"alias_kind":"pith_short_8","alias_value":"UJBTDMCG","created_at":"2026-05-20T00:03:37Z"}],"graph_snapshots":[{"event_id":"sha256:7d5d5ff4ad729e2011811045731da33bec1ed73276e9b5817d7200fc2c8b2b7d","target":"graph","created_at":"2026-05-20T00:03:37Z","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":"Extensive experiments on a real-world residential PV dataset validate the effectiveness of the developed method and demonstrate that it outperforms state-of-the-art FL methods across various scenarios, with scalability across community sizes and strong robustness to class imbalance."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that discrepancies between reported PV generation and fused solar irradiance/weather data reliably indicate fraud, and that prototype alignment sufficiently mitigates class imbalance without distorting normal generation patterns (implicit in the co-attention and FL aggregation description)."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A federated learning framework fuses solar irradiance and PV generation data via co-attention, uses prototype alignment for imbalance, and aggregates models across communities to detect generation fraud while preserving privacy."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A federated learning framework fuses solar irradiance data via co-attention to detect photovoltaic generation fraud while keeping household data private across communities."}],"snapshot_sha256":"3f6e156252b24b4c26143665f74855f1e9e6d2d72f9dec96f2871dacafa831ff"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T20:31:18.921308Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T20:20:42.557293Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"citation_quote_validity","ran_at":"2026-05-19T19:49:41.692432Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T18:51:56.308326Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.160143Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:22.996445Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.17039/integrity.json","findings":[],"snapshot_sha256":"a4ad3a8b6b656cc061000691205101e382f0cdde273496636d4400bf4a3c6577","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The wide adoption of residential photovoltaic (PV) systems introduces new challenges for generation fraud detection (FD). Unlike traditional electricity theft detection, which focuses on electricity consumption-side behavior, PV generation fraud detection (PVG-FD) is complicated by the inherent intermittency and uncertainty of PV generation. The distributed nature of PV systems poses further challenges for centralized PVG-FD approaches due to scalability and privacy concerns. This paper develops a privacy-preserving distributed PVG-FD framework based on federated learning (FL). In this framewo","authors_text":"Chenghao Huang, Hao Wang, Xiaolu Chen, Yanru Zhang","cross_cats":["cs.CE"],"headline":"A federated learning framework fuses solar irradiance data via co-attention to detect photovoltaic generation fraud while keeping household data private across communities.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T15:19:14Z","title":"Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework"},"references":{"count":44,"internal_anchors":0,"resolved_work":44,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"International Energy Agency, “Renewables 2024,” 2023. [Online]. Available: https://www.iea.org/reports/renewables-2024","work_id":"9d20969d-56a8-48d5-992d-6c18d9adb9bb","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Renewable energy market update,","work_id":"d2b1b7c0-e739-4dc4-8d9d-f257cb3054d3","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"A review of cyber security risks of power systems: from static to dynamic false data attacks,","work_id":"fcf1fba8-831a-405b-b6ea-9adf52f8fbf6","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Detection methods in smart meters for electricity thefts: A survey,","work_id":"e263eaeb-f3b5-4b9d-ab94-88beed82073e","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Deep learning detection of electricity theft cyber-attacks in renewable distributed generation,","work_id":"39468152-171b-4eb0-af24-9ec47e012ebc","year":2020}],"snapshot_sha256":"c376420fcbdd6c7bd7a431d499b9c705271f605f82d0a917c580b999f3c7c778"},"source":{"id":"2605.17039","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T20:13:04.550723Z","id":"2aab4dc2-268e-48b5-9c4e-f8d2f4753841","model_set":{"reader":"grok-4.3"},"one_line_summary":"A federated learning framework fuses solar irradiance and PV generation data via co-attention, uses prototype alignment for imbalance, and aggregates models across communities to detect generation fraud while preserving privacy.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A federated learning framework fuses solar irradiance data via co-attention to detect photovoltaic generation fraud while keeping household data private across communities.","strongest_claim":"Extensive experiments on a real-world residential PV dataset validate the effectiveness of the developed method and demonstrate that it outperforms state-of-the-art FL methods across various scenarios, with scalability across community sizes and strong robustness to class imbalance.","weakest_assumption":"The assumption that discrepancies between reported PV generation and fused solar irradiance/weather data reliably indicate fraud, and that prototype alignment sufficiently mitigates class imbalance without distorting normal generation patterns (implicit in the co-attention and FL aggregation description)."}},"verdict_id":"2aab4dc2-268e-48b5-9c4e-f8d2f4753841"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7ee48e09e4281734196ebd3c7f3e4a7fba2dc96978d5e906fe43712030ed6e67","target":"record","created_at":"2026-05-20T00:03:37Z","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":"7a2680599d8a6f20ca330fa05c61dfa3463e2a4843fe4ae57fa0de3d4180f3ad","cross_cats_sorted":["cs.CE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T15:19:14Z","title_canon_sha256":"a1e462e823449e2622158f3d0f312cda22a8399d761a22166e2f5b27e7641de6"},"schema_version":"1.0","source":{"id":"2605.17039","kind":"arxiv","version":1}},"canonical_sha256":"a24331b0460f2e5e40aedaffb4a10371701bc223425013115f83a45f5db982fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a24331b0460f2e5e40aedaffb4a10371701bc223425013115f83a45f5db982fe","first_computed_at":"2026-05-20T00:03:37.273148Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:37.273148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2fmIok1MlUkF9mjYYBQPhvrv6HEfyCRXyeps1W0binZhHVaAnOswxfhK3Px1/TwMgD5j9QJejYOTjaPS7y9oCg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:37.274100Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17039","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7ee48e09e4281734196ebd3c7f3e4a7fba2dc96978d5e906fe43712030ed6e67","sha256:7d5d5ff4ad729e2011811045731da33bec1ed73276e9b5817d7200fc2c8b2b7d"],"state_sha256":"11352f1b95d04074905c1a1b216386dfe50e673ff3785bd008b671819f06d9ce"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C0LHImMM7srM0DDCN9G3kMSTzAN9QjZaMEH6SDenqZNkbZy4DMXlU7DlGEPa+9M40LFAmG4Kn43iLV8IY0xkDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T00:27:31.286602Z","bundle_sha256":"58ef6f1eed3785aa58c5391d995e42f0af79f56a1fb2beec0172b7634bafa902"}}