{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:TXXTO7CIU235FUBIV2SHLMDG3S","short_pith_number":"pith:TXXTO7CI","canonical_record":{"source":{"id":"2605.15471","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-14T23:20:26Z","cross_cats_sorted":[],"title_canon_sha256":"8826147fd4f169ea923f486e7532804cd99cea14d007f5b5f86ea7c3b2aa4116","abstract_canon_sha256":"43162963cf965406d23297719a5af69788c4762dc0abd0c997b0bb496e9dbbb7"},"schema_version":"1.0"},"canonical_sha256":"9def377c48a6b7d2d028aea475b066dc87a6f6d6c1994e4bd3f7b9c136d70fde","source":{"kind":"arxiv","id":"2605.15471","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15471","created_at":"2026-05-20T00:01:00Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15471v1","created_at":"2026-05-20T00:01:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15471","created_at":"2026-05-20T00:01:00Z"},{"alias_kind":"pith_short_12","alias_value":"TXXTO7CIU235","created_at":"2026-05-20T00:01:00Z"},{"alias_kind":"pith_short_16","alias_value":"TXXTO7CIU235FUBI","created_at":"2026-05-20T00:01:00Z"},{"alias_kind":"pith_short_8","alias_value":"TXXTO7CI","created_at":"2026-05-20T00:01:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:TXXTO7CIU235FUBIV2SHLMDG3S","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15471","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-14T23:20:26Z","cross_cats_sorted":[],"title_canon_sha256":"8826147fd4f169ea923f486e7532804cd99cea14d007f5b5f86ea7c3b2aa4116","abstract_canon_sha256":"43162963cf965406d23297719a5af69788c4762dc0abd0c997b0bb496e9dbbb7"},"schema_version":"1.0"},"canonical_sha256":"9def377c48a6b7d2d028aea475b066dc87a6f6d6c1994e4bd3f7b9c136d70fde","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:00.295802Z","signature_b64":"GBQV04/zX9l5hb+YuN9uSZY4OlOwSQuUD95kfYwjB+Ox7GakMnWn6pIY0nfgycMkylG/WgpKpRcAUKeWuBjgBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9def377c48a6b7d2d028aea475b066dc87a6f6d6c1994e4bd3f7b9c136d70fde","last_reissued_at":"2026-05-20T00:01:00.295079Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:00.295079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15471","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":"2026-05-20T00:01:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gBQqTqEWN9sAGM/E4q9kEDuZDYfzxEqSiXLuqggQMtkt9uYY+0cCFR6l7Wd1a6SIH/1JOuEypZ4Rg0EkpjrWAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T22:04:36.200204Z"},"content_sha256":"5afb86e35e6caa8964b3a28886fe6679bd2bc87cdb906f09467110947167110f","schema_version":"1.0","event_id":"sha256:5afb86e35e6caa8964b3a28886fe6679bd2bc87cdb906f09467110947167110f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:TXXTO7CIU235FUBIV2SHLMDG3S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CITYMPC: A Large-Scale Physics-Informed Benchmark and Tool for Generative Complete Multipath Wireless Channel Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"CITYMPC generates complete multipath wireless channel parameters from point-of-view imagery and terrain height maps alone.","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Amitava Ghosh, Ashwin Natraj Arun, Christopher Brinton, David J. Love, David R. Nickel, James V. Krogmeier, Jie Chen, Yaguang Zhang, Yunchou Xing","submitted_at":"2026-05-14T23:20:26Z","abstract_excerpt":"Multipath wireless channels are fully characterized by multipath components (MPCs), including complex channel gain, propagation delay, angle of departure (AoD) and angle of arrival (AoA) in azimuth and elevation. Generating these parameters with the fidelity of ray tracing (RT) remains an open problem. Existing methods either incur the computational cost of RT or require explicit 3D scene geometry at inference. We present CITYMPC, a conditional variational autoencoder (cVAE) that predicts the complete per-path MPC parameter set from point-of-view imagery and terrain height maps alone, achievin"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"CITYMPC predicts the complete per-path MPC parameter set from point-of-view imagery and terrain height maps alone, achieving environment-aware channel generation without access to any three-dimensional scene geometry at inference.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That point-of-view imagery and terrain height maps alone contain enough information to accurately reconstruct the full set of multipath component parameters that would otherwise require explicit 3D scene geometry.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"CITYMPC, a cVAE model, predicts full per-path multipath component parameters from POV images and height maps alone, matching ray-tracing accuracy with 1.29 dB power MAE and 7.25 ns delay MAE across 427k links in five cities while releasing the dataset as a benchmark.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"CITYMPC generates complete multipath wireless channel parameters from point-of-view imagery and terrain height maps alone.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"a67e1c3f96d77eb7324a2c6153710fda96cdf19caa703c9cd11865b1c5731bbd"},"source":{"id":"2605.15471","kind":"arxiv","version":1},"verdict":{"id":"ca7bd7d8-2195-46cc-bf57-a6fc86ed1efe","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T14:30:31.088545Z","strongest_claim":"CITYMPC predicts the complete per-path MPC parameter set from point-of-view imagery and terrain height maps alone, achieving environment-aware channel generation without access to any three-dimensional scene geometry at inference.","one_line_summary":"CITYMPC, a cVAE model, predicts full per-path multipath component parameters from POV images and height maps alone, matching ray-tracing accuracy with 1.29 dB power MAE and 7.25 ns delay MAE across 427k links in five cities while releasing the dataset as a benchmark.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That point-of-view imagery and terrain height maps alone contain enough information to accurately reconstruct the full set of multipath component parameters that would otherwise require explicit 3D scene geometry.","pith_extraction_headline":"CITYMPC generates complete multipath wireless channel parameters from point-of-view imagery and terrain height maps alone."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15471/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"cited_work_retraction","ran_at":"2026-05-19T15:22:18.644213Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T15:01:17.572356Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T14:37:49.817424Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.088286Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T13:49:41.410964Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.662781Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"f1b4265d92ebc3da1819ee3bb9f81d45d59117265d263317e9d80ece5c83a3d7"},"references":{"count":56,"sample":[{"doi":"","year":2030,"title":"Recommendation M.2160: Framework and Overall Objectives of the Future Devel- opment of IMT for 2030 and Beyond, 2023","work_id":"30212b07-95c0-4029-891a-b34d980b93e7","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"6G Roadmap for Vertical Industries, 2023","work_id":"bff6c333-2057-4ecb-9404-b90f247f50a7","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/mcom.002.2400150","year":2025,"title":"Brinton, Mung Chiang, Kwang Taik Kim, David J","work_id":"bd5ea572-1404-4971-80ee-d9202cfbe569","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/mwc.2026.3678210","year":2026,"title":"Hwanjin Kim, Junil Choi, and David J. Love. Machine-Learning Techniques for Wireless Channel Prediction: Insights and Practical Guidance. IEEE Wireless Communications, pages 1–8, 2026. doi: 10.1109/MW","work_id":"ae0956b4-bdaa-4236-9a98-72e72e88fe58","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Radio Propagation Measurements and Channel Modeling","work_id":"5e534c9e-13ff-4eae-bab9-b11405002486","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":56,"snapshot_sha256":"eb16d724be48a657036d608b18d98a0e16053e38c9c6f8971fbdcc04b3f929dc","internal_anchors":5},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c2bf02524e5d6316f545c6692f27f270f603e7607bbdb3092d33aa692defec59"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"ca7bd7d8-2195-46cc-bf57-a6fc86ed1efe"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"apEkNX2l7tNCNvMKsNOJluNNmXO1S/DDiKNC3vGePf4avgPxXESfRDgbZiRw5z7AE+AwbxNu0LA/Y12GUGTrCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T22:04:36.201565Z"},"content_sha256":"07a123b7d7365d4cacae1e110b89f3ec247fc5d2c6538aea541fea5d90748f79","schema_version":"1.0","event_id":"sha256:07a123b7d7365d4cacae1e110b89f3ec247fc5d2c6538aea541fea5d90748f79"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TXXTO7CIU235FUBIV2SHLMDG3S/bundle.json","state_url":"https://pith.science/pith/TXXTO7CIU235FUBIV2SHLMDG3S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TXXTO7CIU235FUBIV2SHLMDG3S/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":"2026-05-24T22:04:36Z","links":{"resolver":"https://pith.science/pith/TXXTO7CIU235FUBIV2SHLMDG3S","bundle":"https://pith.science/pith/TXXTO7CIU235FUBIV2SHLMDG3S/bundle.json","state":"https://pith.science/pith/TXXTO7CIU235FUBIV2SHLMDG3S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TXXTO7CIU235FUBIV2SHLMDG3S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TXXTO7CIU235FUBIV2SHLMDG3S","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":"43162963cf965406d23297719a5af69788c4762dc0abd0c997b0bb496e9dbbb7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-14T23:20:26Z","title_canon_sha256":"8826147fd4f169ea923f486e7532804cd99cea14d007f5b5f86ea7c3b2aa4116"},"schema_version":"1.0","source":{"id":"2605.15471","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15471","created_at":"2026-05-20T00:01:00Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15471v1","created_at":"2026-05-20T00:01:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15471","created_at":"2026-05-20T00:01:00Z"},{"alias_kind":"pith_short_12","alias_value":"TXXTO7CIU235","created_at":"2026-05-20T00:01:00Z"},{"alias_kind":"pith_short_16","alias_value":"TXXTO7CIU235FUBI","created_at":"2026-05-20T00:01:00Z"},{"alias_kind":"pith_short_8","alias_value":"TXXTO7CI","created_at":"2026-05-20T00:01:00Z"}],"graph_snapshots":[{"event_id":"sha256:07a123b7d7365d4cacae1e110b89f3ec247fc5d2c6538aea541fea5d90748f79","target":"graph","created_at":"2026-05-20T00:01:00Z","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":"CITYMPC predicts the complete per-path MPC parameter set from point-of-view imagery and terrain height maps alone, achieving environment-aware channel generation without access to any three-dimensional scene geometry at inference."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That point-of-view imagery and terrain height maps alone contain enough information to accurately reconstruct the full set of multipath component parameters that would otherwise require explicit 3D scene geometry."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"CITYMPC, a cVAE model, predicts full per-path multipath component parameters from POV images and height maps alone, matching ray-tracing accuracy with 1.29 dB power MAE and 7.25 ns delay MAE across 427k links in five cities while releasing the dataset as a benchmark."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"CITYMPC generates complete multipath wireless channel parameters from point-of-view imagery and terrain height maps alone."}],"snapshot_sha256":"a67e1c3f96d77eb7324a2c6153710fda96cdf19caa703c9cd11865b1c5731bbd"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c2bf02524e5d6316f545c6692f27f270f603e7607bbdb3092d33aa692defec59"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T15:22:18.644213Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T15:01:17.572356Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T14:37:49.817424Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.088286Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"citation_quote_validity","ran_at":"2026-05-19T13:49:41.410964Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.662781Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.15471/integrity.json","findings":[],"snapshot_sha256":"f1b4265d92ebc3da1819ee3bb9f81d45d59117265d263317e9d80ece5c83a3d7","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multipath wireless channels are fully characterized by multipath components (MPCs), including complex channel gain, propagation delay, angle of departure (AoD) and angle of arrival (AoA) in azimuth and elevation. Generating these parameters with the fidelity of ray tracing (RT) remains an open problem. Existing methods either incur the computational cost of RT or require explicit 3D scene geometry at inference. We present CITYMPC, a conditional variational autoencoder (cVAE) that predicts the complete per-path MPC parameter set from point-of-view imagery and terrain height maps alone, achievin","authors_text":"Amitava Ghosh, Ashwin Natraj Arun, Christopher Brinton, David J. Love, David R. Nickel, James V. Krogmeier, Jie Chen, Yaguang Zhang, Yunchou Xing","cross_cats":[],"headline":"CITYMPC generates complete multipath wireless channel parameters from point-of-view imagery and terrain height maps alone.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-14T23:20:26Z","title":"CITYMPC: A Large-Scale Physics-Informed Benchmark and Tool for Generative Complete Multipath Wireless Channel Modeling"},"references":{"count":56,"internal_anchors":5,"resolved_work":56,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Recommendation M.2160: Framework and Overall Objectives of the Future Devel- opment of IMT for 2030 and Beyond, 2023","work_id":"30212b07-95c0-4029-891a-b34d980b93e7","year":2030},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"6G Roadmap for Vertical Industries, 2023","work_id":"bff6c333-2057-4ecb-9404-b90f247f50a7","year":2023},{"cited_arxiv_id":"","doi":"10.1109/mcom.002.2400150","is_internal_anchor":false,"ref_index":3,"title":"Brinton, Mung Chiang, Kwang Taik Kim, David J","work_id":"bd5ea572-1404-4971-80ee-d9202cfbe569","year":2025},{"cited_arxiv_id":"","doi":"10.1109/mwc.2026.3678210","is_internal_anchor":false,"ref_index":4,"title":"Hwanjin Kim, Junil Choi, and David J. Love. Machine-Learning Techniques for Wireless Channel Prediction: Insights and Practical Guidance. IEEE Wireless Communications, pages 1–8, 2026. doi: 10.1109/MW","work_id":"ae0956b4-bdaa-4236-9a98-72e72e88fe58","year":2026},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Radio Propagation Measurements and Channel Modeling","work_id":"5e534c9e-13ff-4eae-bab9-b11405002486","year":2022}],"snapshot_sha256":"eb16d724be48a657036d608b18d98a0e16053e38c9c6f8971fbdcc04b3f929dc"},"source":{"id":"2605.15471","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T14:30:31.088545Z","id":"ca7bd7d8-2195-46cc-bf57-a6fc86ed1efe","model_set":{"reader":"grok-4.3"},"one_line_summary":"CITYMPC, a cVAE model, predicts full per-path multipath component parameters from POV images and height maps alone, matching ray-tracing accuracy with 1.29 dB power MAE and 7.25 ns delay MAE across 427k links in five cities while releasing the dataset as a benchmark.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"CITYMPC generates complete multipath wireless channel parameters from point-of-view imagery and terrain height maps alone.","strongest_claim":"CITYMPC predicts the complete per-path MPC parameter set from point-of-view imagery and terrain height maps alone, achieving environment-aware channel generation without access to any three-dimensional scene geometry at inference.","weakest_assumption":"That point-of-view imagery and terrain height maps alone contain enough information to accurately reconstruct the full set of multipath component parameters that would otherwise require explicit 3D scene geometry."}},"verdict_id":"ca7bd7d8-2195-46cc-bf57-a6fc86ed1efe"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5afb86e35e6caa8964b3a28886fe6679bd2bc87cdb906f09467110947167110f","target":"record","created_at":"2026-05-20T00:01:00Z","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":"43162963cf965406d23297719a5af69788c4762dc0abd0c997b0bb496e9dbbb7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-14T23:20:26Z","title_canon_sha256":"8826147fd4f169ea923f486e7532804cd99cea14d007f5b5f86ea7c3b2aa4116"},"schema_version":"1.0","source":{"id":"2605.15471","kind":"arxiv","version":1}},"canonical_sha256":"9def377c48a6b7d2d028aea475b066dc87a6f6d6c1994e4bd3f7b9c136d70fde","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9def377c48a6b7d2d028aea475b066dc87a6f6d6c1994e4bd3f7b9c136d70fde","first_computed_at":"2026-05-20T00:01:00.295079Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:00.295079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GBQV04/zX9l5hb+YuN9uSZY4OlOwSQuUD95kfYwjB+Ox7GakMnWn6pIY0nfgycMkylG/WgpKpRcAUKeWuBjgBg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:00.295802Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15471","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5afb86e35e6caa8964b3a28886fe6679bd2bc87cdb906f09467110947167110f","sha256:07a123b7d7365d4cacae1e110b89f3ec247fc5d2c6538aea541fea5d90748f79"],"state_sha256":"26b563bd5b890109c8fadd5b77db957b430022e23cba3460a0418ea6b04fd6c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7s3FJ9r+2rkgWZZtAnZ3ChmAC4EUlW6s6OsXn+t5Hp/o9uWm6Z41WZse3WmnJlMfIYbg4XYps14HlrxjUqq3BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T22:04:36.206865Z","bundle_sha256":"792aa915ab91abc85d8136171e885389432a8aa465eefcfb8b76a3a90469fb62"}}