{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:NEBDNYI7SUCWMT64C77G2ZSFAA","short_pith_number":"pith:NEBDNYI7","canonical_record":{"source":{"id":"2605.12762","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T21:17:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"97a5f8af62bdfc03879f9b8df485d51738456c8ee8c31a789ebc49e2fdcae8ff","abstract_canon_sha256":"50e23d3ed7a4fbe2512f885c4e0c789dd4bbc75f03380b6a3f92cbbaf28bcaaa"},"schema_version":"1.0"},"canonical_sha256":"690236e11f9505664fdc17fe6d66450032a617f0e5b5584791b3411e4df99898","source":{"kind":"arxiv","id":"2605.12762","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12762","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12762v1","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12762","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"pith_short_12","alias_value":"NEBDNYI7SUCW","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"NEBDNYI7SUCWMT64","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"NEBDNYI7","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:NEBDNYI7SUCWMT64C77G2ZSFAA","target":"record","payload":{"canonical_record":{"source":{"id":"2605.12762","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T21:17:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"97a5f8af62bdfc03879f9b8df485d51738456c8ee8c31a789ebc49e2fdcae8ff","abstract_canon_sha256":"50e23d3ed7a4fbe2512f885c4e0c789dd4bbc75f03380b6a3f92cbbaf28bcaaa"},"schema_version":"1.0"},"canonical_sha256":"690236e11f9505664fdc17fe6d66450032a617f0e5b5584791b3411e4df99898","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:09:48.373175Z","signature_b64":"LyUzj8oWasTNEFjIRONq+KxlCuSUXiFio+9hEpIH63QjlpnVI92tRhStEQnRgunqa/vUfVJmbq4Na1bJXmYVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"690236e11f9505664fdc17fe6d66450032a617f0e5b5584791b3411e4df99898","last_reissued_at":"2026-05-18T03:09:48.372310Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:09:48.372310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.12762","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":"2026-05-18T03:09:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VhX57UqTrJH02gXsJKI/AOG4sK8yTuAuv9JDyb5wldWvC6/vCp47Z5jOcmFU6zJUXPQkpbsfwgUFgiuNIFILCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T00:10:32.346070Z"},"content_sha256":"69d91324c5e36ec6de4afc0b90b7a756e645cfeeee175807c1dea165428ee555","schema_version":"1.0","event_id":"sha256:69d91324c5e36ec6de4afc0b90b7a756e645cfeeee175807c1dea165428ee555"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:NEBDNYI7SUCWMT64C77G2ZSFAA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Quantile Regression for Extreme Precipitation Downscaling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A multi-quantile super-resolution network detects extreme precipitation up to 18 times better than deterministic baselines by using pinball loss on separate quantile heads.","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Gareth Lagerwall, Hamed Najafi, Jason Liu, Jayantha Obeysekera","submitted_at":"2026-05-12T21:17:26Z","abstract_excerpt":"Deep super-resolution networks for precipitation downscaling achieve strong bulk skill yet systematically under-predict the heavy-tail events that drive flood risk. We demonstrate that the primary obstacle is the loss function, not the data: under intensity-weighted MAE, real and synthetic labels at the same input are simply averaged, meaning data augmentation shifts the predicted mean rather than the conditional distribution. We resolve this with Q-SRDRN, a multi-quantile super-resolution network trained with pinball loss at tau in 0.50, 0.95, 0.99, 0.999. Two CNN-specific design choices make"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Under this design, data augmentation via cVAE becomes complementary: the median head absorbs synthetic patterns without contaminating upper quantiles. Empirically, on Florida (convective/tropical-cyclone dominated), the un-augmented Q-SRDRN P999 head detects 1,598 of 2,111 events at 200 mm/day versus 88 for the deterministic baseline--an 18x detection-rate gain (4.2% to 75.7%)--with 63% lower KL divergence and 3.9% lower RMSE.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The primary obstacle is the loss function, not the data: under intensity-weighted MAE, real and synthetic labels at the same input are simply averaged, meaning data augmentation shifts the predicted mean rather than the conditional distribution.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A multi-quantile super-resolution network detects extreme precipitation up to 18 times better than deterministic baselines by using pinball loss on separate quantile heads.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ac1c872a908ee5d276ed5fae9bd0ddae4cf918cfae8c8d2d85f8f88748bb7025"},"source":{"id":"2605.12762","kind":"arxiv","version":1},"verdict":{"id":"d87848b5-b7b2-443d-a758-d42f3902d6b6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T21:05:01.913654Z","strongest_claim":"Under this design, data augmentation via cVAE becomes complementary: the median head absorbs synthetic patterns without contaminating upper quantiles. Empirically, on Florida (convective/tropical-cyclone dominated), the un-augmented Q-SRDRN P999 head detects 1,598 of 2,111 events at 200 mm/day versus 88 for the deterministic baseline--an 18x detection-rate gain (4.2% to 75.7%)--with 63% lower KL divergence and 3.9% lower RMSE.","one_line_summary":"Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The primary obstacle is the loss function, not the data: under intensity-weighted MAE, real and synthetic labels at the same input are simply averaged, meaning data augmentation shifts the predicted mean rather than the conditional distribution.","pith_extraction_headline":"A multi-quantile super-resolution network detects extreme precipitation up to 18 times better than deterministic baselines by using pinball loss on separate quantile heads."},"references":{"count":30,"sample":[{"doi":"10.5194/gmd-13-2109-2020","year":2020,"title":"J. Ba\\ no-Medina, R. Manzanas, and J. M. Guti \\'e rrez. Configuration and intercomparison of deep learning neural models for statistical downscaling. Geoscientific Model Development, 13(4):2109--2124,","work_id":"1e183748-4e3e-4a6d-ac2a-80e340040e53","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1038/s41586-023-06185-3","year":2023,"title":"Accurate medium-range global weather forecasting with 3d neural networks","work_id":"2b852ba0-c3f8-4951-a669-5611c50c82be","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"A. Brando, J. Gimeno, J. A. Rodr\\'iguez-Serrano, and J. Vitri\\`a. Deep non-crossing quantiles through the partial derivative. In Proceedings of the 25th International Conference on Artificial Intellig","work_id":"6c1cf97b-cccd-4a83-84ec-cb4f9235c54d","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1175/1520-0493(2004)132","year":2004,"title":"J. B. Bremnes. Probabilistic forecasts of precipitation in terms of quantiles using NWP model output. Monthly Weather Review, 132(1):338--347, 2004. DOI: 10.1175/1520-0493(2004)132<0338:PFOPIT>2.0.CO;","work_id":"9d05414e-6735-493c-afb4-86961bdd43c3","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1016/j.cageo.2010.07.005","year":2011,"title":"A. J. Cannon. Quantile regression neural networks: Implementation in R and application to precipitation downscaling. Computers & Geosciences, 37(9):1277--1284, 2011. DOI: 10.1016/j.cageo.2010.07.005 h","work_id":"745f0b9d-1845-4f2f-bd84-52cf6ca2eaaf","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":30,"snapshot_sha256":"3220e8854da9df8b62e587cffd286f3b55da760bc31c9752cf74cd88412b48bb","internal_anchors":3},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a661b0dfd1017d89c6d9f3f23daa9f732c8921960345504a629f505d574c3b7d"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"d87848b5-b7b2-443d-a758-d42f3902d6b6"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:09:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"quDjomoTn5KzVh58g06aRuJgLOpWWx863YM7e3rRBVFEtoB272ugUHwflWoJe52foZu3J0YIjn6OuuxKFeA7Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T00:10:32.347114Z"},"content_sha256":"3e62eb152f81dcabd0a0ed1caffab873065bc7a4560928475fdc939bd6f7d0ab","schema_version":"1.0","event_id":"sha256:3e62eb152f81dcabd0a0ed1caffab873065bc7a4560928475fdc939bd6f7d0ab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NEBDNYI7SUCWMT64C77G2ZSFAA/bundle.json","state_url":"https://pith.science/pith/NEBDNYI7SUCWMT64C77G2ZSFAA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NEBDNYI7SUCWMT64C77G2ZSFAA/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":"2026-05-30T00:10:32Z","links":{"resolver":"https://pith.science/pith/NEBDNYI7SUCWMT64C77G2ZSFAA","bundle":"https://pith.science/pith/NEBDNYI7SUCWMT64C77G2ZSFAA/bundle.json","state":"https://pith.science/pith/NEBDNYI7SUCWMT64C77G2ZSFAA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NEBDNYI7SUCWMT64C77G2ZSFAA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:NEBDNYI7SUCWMT64C77G2ZSFAA","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":"50e23d3ed7a4fbe2512f885c4e0c789dd4bbc75f03380b6a3f92cbbaf28bcaaa","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T21:17:26Z","title_canon_sha256":"97a5f8af62bdfc03879f9b8df485d51738456c8ee8c31a789ebc49e2fdcae8ff"},"schema_version":"1.0","source":{"id":"2605.12762","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12762","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12762v1","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12762","created_at":"2026-05-18T03:09:48Z"},{"alias_kind":"pith_short_12","alias_value":"NEBDNYI7SUCW","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"NEBDNYI7SUCWMT64","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"NEBDNYI7","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:3e62eb152f81dcabd0a0ed1caffab873065bc7a4560928475fdc939bd6f7d0ab","target":"graph","created_at":"2026-05-18T03:09:48Z","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":"Under this design, data augmentation via cVAE becomes complementary: the median head absorbs synthetic patterns without contaminating upper quantiles. Empirically, on Florida (convective/tropical-cyclone dominated), the un-augmented Q-SRDRN P999 head detects 1,598 of 2,111 events at 200 mm/day versus 88 for the deterministic baseline--an 18x detection-rate gain (4.2% to 75.7%)--with 63% lower KL divergence and 3.9% lower RMSE."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The primary obstacle is the loss function, not the data: under intensity-weighted MAE, real and synthetic labels at the same input are simply averaged, meaning data augmentation shifts the predicted mean rather than the conditional distribution."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A multi-quantile super-resolution network detects extreme precipitation up to 18 times better than deterministic baselines by using pinball loss on separate quantile heads."}],"snapshot_sha256":"ac1c872a908ee5d276ed5fae9bd0ddae4cf918cfae8c8d2d85f8f88748bb7025"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a661b0dfd1017d89c6d9f3f23daa9f732c8921960345504a629f505d574c3b7d"},"paper":{"abstract_excerpt":"Deep super-resolution networks for precipitation downscaling achieve strong bulk skill yet systematically under-predict the heavy-tail events that drive flood risk. We demonstrate that the primary obstacle is the loss function, not the data: under intensity-weighted MAE, real and synthetic labels at the same input are simply averaged, meaning data augmentation shifts the predicted mean rather than the conditional distribution. We resolve this with Q-SRDRN, a multi-quantile super-resolution network trained with pinball loss at tau in 0.50, 0.95, 0.99, 0.999. Two CNN-specific design choices make","authors_text":"Gareth Lagerwall, Hamed Najafi, Jason Liu, Jayantha Obeysekera","cross_cats":["cs.AI"],"headline":"A multi-quantile super-resolution network detects extreme precipitation up to 18 times better than deterministic baselines by using pinball loss on separate quantile heads.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T21:17:26Z","title":"Multi-Quantile Regression for Extreme Precipitation Downscaling"},"references":{"count":30,"internal_anchors":3,"resolved_work":30,"sample":[{"cited_arxiv_id":"","doi":"10.5194/gmd-13-2109-2020","is_internal_anchor":false,"ref_index":1,"title":"J. Ba\\ no-Medina, R. Manzanas, and J. M. Guti \\'e rrez. Configuration and intercomparison of deep learning neural models for statistical downscaling. Geoscientific Model Development, 13(4):2109--2124,","work_id":"1e183748-4e3e-4a6d-ac2a-80e340040e53","year":2020},{"cited_arxiv_id":"","doi":"10.1038/s41586-023-06185-3","is_internal_anchor":false,"ref_index":2,"title":"Accurate medium-range global weather forecasting with 3d neural networks","work_id":"2b852ba0-c3f8-4951-a669-5611c50c82be","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"A. Brando, J. Gimeno, J. A. Rodr\\'iguez-Serrano, and J. Vitri\\`a. Deep non-crossing quantiles through the partial derivative. In Proceedings of the 25th International Conference on Artificial Intellig","work_id":"6c1cf97b-cccd-4a83-84ec-cb4f9235c54d","year":2022},{"cited_arxiv_id":"","doi":"10.1175/1520-0493(2004)132","is_internal_anchor":false,"ref_index":4,"title":"J. B. Bremnes. Probabilistic forecasts of precipitation in terms of quantiles using NWP model output. Monthly Weather Review, 132(1):338--347, 2004. DOI: 10.1175/1520-0493(2004)132<0338:PFOPIT>2.0.CO;","work_id":"9d05414e-6735-493c-afb4-86961bdd43c3","year":2004},{"cited_arxiv_id":"","doi":"10.1016/j.cageo.2010.07.005","is_internal_anchor":false,"ref_index":5,"title":"A. J. Cannon. Quantile regression neural networks: Implementation in R and application to precipitation downscaling. Computers & Geosciences, 37(9):1277--1284, 2011. DOI: 10.1016/j.cageo.2010.07.005 h","work_id":"745f0b9d-1845-4f2f-bd84-52cf6ca2eaaf","year":2011}],"snapshot_sha256":"3220e8854da9df8b62e587cffd286f3b55da760bc31c9752cf74cd88412b48bb"},"source":{"id":"2605.12762","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T21:05:01.913654Z","id":"d87848b5-b7b2-443d-a758-d42f3902d6b6","model_set":{"reader":"grok-4.3"},"one_line_summary":"Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A multi-quantile super-resolution network detects extreme precipitation up to 18 times better than deterministic baselines by using pinball loss on separate quantile heads.","strongest_claim":"Under this design, data augmentation via cVAE becomes complementary: the median head absorbs synthetic patterns without contaminating upper quantiles. Empirically, on Florida (convective/tropical-cyclone dominated), the un-augmented Q-SRDRN P999 head detects 1,598 of 2,111 events at 200 mm/day versus 88 for the deterministic baseline--an 18x detection-rate gain (4.2% to 75.7%)--with 63% lower KL divergence and 3.9% lower RMSE.","weakest_assumption":"The primary obstacle is the loss function, not the data: under intensity-weighted MAE, real and synthetic labels at the same input are simply averaged, meaning data augmentation shifts the predicted mean rather than the conditional distribution."}},"verdict_id":"d87848b5-b7b2-443d-a758-d42f3902d6b6"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:69d91324c5e36ec6de4afc0b90b7a756e645cfeeee175807c1dea165428ee555","target":"record","created_at":"2026-05-18T03:09:48Z","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":"50e23d3ed7a4fbe2512f885c4e0c789dd4bbc75f03380b6a3f92cbbaf28bcaaa","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T21:17:26Z","title_canon_sha256":"97a5f8af62bdfc03879f9b8df485d51738456c8ee8c31a789ebc49e2fdcae8ff"},"schema_version":"1.0","source":{"id":"2605.12762","kind":"arxiv","version":1}},"canonical_sha256":"690236e11f9505664fdc17fe6d66450032a617f0e5b5584791b3411e4df99898","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"690236e11f9505664fdc17fe6d66450032a617f0e5b5584791b3411e4df99898","first_computed_at":"2026-05-18T03:09:48.372310Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:48.372310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LyUzj8oWasTNEFjIRONq+KxlCuSUXiFio+9hEpIH63QjlpnVI92tRhStEQnRgunqa/vUfVJmbq4Na1bJXmYVDw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:48.373175Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12762","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:69d91324c5e36ec6de4afc0b90b7a756e645cfeeee175807c1dea165428ee555","sha256:3e62eb152f81dcabd0a0ed1caffab873065bc7a4560928475fdc939bd6f7d0ab"],"state_sha256":"f16c79b8b8281385a3f18279fe58c647242ce3def727ad4684e9d3ce4e1801bf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yAtpz8ppbCZcuKvQ5vJE4YkL7UbI0gwE3ds5LYI+rd4q86wBDcZAK658Co4kK+yC898gWGTRyYZ2ETbaDj6GCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T00:10:32.351910Z","bundle_sha256":"a8a67007607b737bc645e1c33d627d7a1c171895d1fffd5c192c2304e86dc87d"}}