{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:BVQCOUBMRT42SL6GHJBKFNTKED","short_pith_number":"pith:BVQCOUBM","canonical_record":{"source":{"id":"2605.12567","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-12T09:27:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"77c5da0b3ac853cbe93fa364ade39354eea4e48b2d4cbb2f28553185be13919a","abstract_canon_sha256":"7650691334ac4d0d8bbdb821da603a4adf9df0ff0fff7d60c1a3e5c011fd3dc4"},"schema_version":"1.0"},"canonical_sha256":"0d6027502c8cf9a92fc63a42a2b66a20c99ce5696ed241011060229238e4a4b8","source":{"kind":"arxiv","id":"2605.12567","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12567","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12567v1","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12567","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"pith_short_12","alias_value":"BVQCOUBMRT42","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"BVQCOUBMRT42SL6G","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"BVQCOUBM","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:BVQCOUBMRT42SL6GHJBKFNTKED","target":"record","payload":{"canonical_record":{"source":{"id":"2605.12567","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-12T09:27:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"77c5da0b3ac853cbe93fa364ade39354eea4e48b2d4cbb2f28553185be13919a","abstract_canon_sha256":"7650691334ac4d0d8bbdb821da603a4adf9df0ff0fff7d60c1a3e5c011fd3dc4"},"schema_version":"1.0"},"canonical_sha256":"0d6027502c8cf9a92fc63a42a2b66a20c99ce5696ed241011060229238e4a4b8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:10:01.840502Z","signature_b64":"S28HiUvlp3F2q8xPP6OA7+vQAs0DvTbnxnBIKTucLPuj0EYrH2dqofZ34VFX8evVJV49mxQpFrLtoQBVycnGAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d6027502c8cf9a92fc63a42a2b66a20c99ce5696ed241011060229238e4a4b8","last_reissued_at":"2026-05-18T03:10:01.839807Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:10:01.839807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.12567","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":"N9U04G9LUlm4wRklekAnzlnDdHjKUc0ovuY7i6l5tYka9Mv/J4IZPQ76R1+RxhYfzEEA8drtBmTbzTnvCIdGDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T17:06:10.479543Z"},"content_sha256":"9b196d99a57d90b3a23ff7ab739edf5a5103c8457cc0690479d554c3dd811919","schema_version":"1.0","event_id":"sha256:9b196d99a57d90b3a23ff7ab739edf5a5103c8457cc0690479d554c3dd811919"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:BVQCOUBMRT42SL6GHJBKFNTKED","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pyramid Self-contrastive Learning Framework for Test-time Ultrasound Image Denoising","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Self-contrastive learning on sub-aperture signals produces denoised ultrasound images from a single test sample.","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bingze Dai, Jiajing Zhang, Wei-Ning Lee, Xi Zhang, Yue Xu","submitted_at":"2026-05-12T09:27:21Z","abstract_excerpt":"The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit noise assumptions whose validity diminishes under composite noise conditions. Learning-based methods require massive labeled data and model parameters. These pre-defined and pre-trained manners entail an inevitable domain shift in complex in vivo environments, so they are limited to a specific noise type and often blur structural details. In this study, we propose a pure test-time training framework for one-shot ultrasound image denoising and apply"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our Aperture-to-Aperture (A2A) framework disentangles anatomical similarity and noise randomness from shuffled sub-apertures through self-contrastive learning in pyramid latent spaces. The clean image is then decoded from the anatomy space, while discarding the noise space. Simulation experiments demonstrated an improvement of 69.3% SNR and 34.4% CNR by A2A. The in vivo results showed 84.8% SNR and 25.7% CNR gains using only two aperture data.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That sub-aperture signals in synthetic aperture ultrasound share sufficient anatomical content while differing primarily in independent noise realizations, allowing reliable disentanglement via self-contrastive learning in pyramid spaces even for complex in vivo composite noise without additional regularization or failure modes.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A2A achieves one-shot ultrasound denoising via pyramid self-contrastive learning on sub-aperture signals to disentangle anatomy from noise, yielding large SNR and CNR gains in simulations and in vivo scans.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Self-contrastive learning on sub-aperture signals produces denoised ultrasound images from a single test sample.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3b3ba93090d562b27ae53fa0464822e6f87a0e827a1df4550f37cf822863b362"},"source":{"id":"2605.12567","kind":"arxiv","version":1},"verdict":{"id":"c2c5ae81-eab7-4e6a-81ed-8e51e4b314f1","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T20:57:03.150351Z","strongest_claim":"Our Aperture-to-Aperture (A2A) framework disentangles anatomical similarity and noise randomness from shuffled sub-apertures through self-contrastive learning in pyramid latent spaces. The clean image is then decoded from the anatomy space, while discarding the noise space. Simulation experiments demonstrated an improvement of 69.3% SNR and 34.4% CNR by A2A. The in vivo results showed 84.8% SNR and 25.7% CNR gains using only two aperture data.","one_line_summary":"A2A achieves one-shot ultrasound denoising via pyramid self-contrastive learning on sub-aperture signals to disentangle anatomy from noise, yielding large SNR and CNR gains in simulations and in vivo scans.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That sub-aperture signals in synthetic aperture ultrasound share sufficient anatomical content while differing primarily in independent noise realizations, allowing reliable disentanglement via self-contrastive learning in pyramid spaces even for complex in vivo composite noise without additional regularization or failure modes.","pith_extraction_headline":"Self-contrastive learning on sub-aperture signals produces denoised ultrasound images from a single test sample."},"references":{"count":68,"sample":[{"doi":"","year":2006,"title":"J. A. Jensen, S. I. Nikolov, K. L. Gammelmark, M. H. Pedersen, Syntheticapertureultrasoundimaging,Ultrasonics44(2006)e5–e15","work_id":"2fc608b0-e044-4d2e-822f-5bf04f89b740","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2014,"title":"C. Papadacci, M. Pernot, M. Couade, M. Fink, M. Tanter, High- contrastultrafastimagingoftheheart,IEEEtransactionsonultrason- ics, ferroelectrics, and frequency control 61 (2) (2014) 288–301","work_id":"be2f8727-66e2-41f2-9c0f-66339dbf1347","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2005,"title":"K. Krissian, R. Kikinis, C.-F. Westin, K. Vosburgh, Speckle- constrained filtering of ultrasound images, in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05),","work_id":"3882f29b-6e3e-4cb1-b1e8-0f02422d6d04","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2002,"title":"H. Xie, L. E. Pierce, F. T. Ulaby, Statistical properties of logarith- mically transformed speckle, IEEE transactions on geoscience and remote sensing 40 (3) (2002) 721–727","work_id":"0931a0c2-7ff5-4f4d-8ad0-a7f6853a7255","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1987,"title":"T. Loupas, W. McDicken, P. Allan, Noise reduction in ultrasonic images by digital filtering, The British journal of radiology 60 (712) (1987) 389–392","work_id":"6f36b4c8-5ba9-4f7d-a3d5-275d499f4b8b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":68,"snapshot_sha256":"ba988434bc4a3bb33dc9858cdccb4e6cbc56e7259c1fdce2b4c6c6f11e958d84","internal_anchors":2},"formal_canon":{"evidence_count":1,"snapshot_sha256":"5d11a1761b0028d439a9f913006ee873bd0727d434448a1b109670a5bec7f6d7"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"c2c5ae81-eab7-4e6a-81ed-8e51e4b314f1"},"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":"DheeMrPcgpd/EWhoMCn8a713qsE3pF0zqsYo7wVJMq/4RPpsA+NNkOD2Sek/PAwrGPzbdvqJDprSpt5JSqUjDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T17:06:10.480180Z"},"content_sha256":"74eb960cff75d589827766bbdbb2e3a3428af92994944150e47f9b8324aefea8","schema_version":"1.0","event_id":"sha256:74eb960cff75d589827766bbdbb2e3a3428af92994944150e47f9b8324aefea8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BVQCOUBMRT42SL6GHJBKFNTKED/bundle.json","state_url":"https://pith.science/pith/BVQCOUBMRT42SL6GHJBKFNTKED/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BVQCOUBMRT42SL6GHJBKFNTKED/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":"2026-05-19T17:06:10Z","links":{"resolver":"https://pith.science/pith/BVQCOUBMRT42SL6GHJBKFNTKED","bundle":"https://pith.science/pith/BVQCOUBMRT42SL6GHJBKFNTKED/bundle.json","state":"https://pith.science/pith/BVQCOUBMRT42SL6GHJBKFNTKED/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BVQCOUBMRT42SL6GHJBKFNTKED/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:BVQCOUBMRT42SL6GHJBKFNTKED","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":"7650691334ac4d0d8bbdb821da603a4adf9df0ff0fff7d60c1a3e5c011fd3dc4","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-12T09:27:21Z","title_canon_sha256":"77c5da0b3ac853cbe93fa364ade39354eea4e48b2d4cbb2f28553185be13919a"},"schema_version":"1.0","source":{"id":"2605.12567","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12567","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12567v1","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12567","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"pith_short_12","alias_value":"BVQCOUBMRT42","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"BVQCOUBMRT42SL6G","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"BVQCOUBM","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:74eb960cff75d589827766bbdbb2e3a3428af92994944150e47f9b8324aefea8","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":"Our Aperture-to-Aperture (A2A) framework disentangles anatomical similarity and noise randomness from shuffled sub-apertures through self-contrastive learning in pyramid latent spaces. The clean image is then decoded from the anatomy space, while discarding the noise space. Simulation experiments demonstrated an improvement of 69.3% SNR and 34.4% CNR by A2A. The in vivo results showed 84.8% SNR and 25.7% CNR gains using only two aperture data."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That sub-aperture signals in synthetic aperture ultrasound share sufficient anatomical content while differing primarily in independent noise realizations, allowing reliable disentanglement via self-contrastive learning in pyramid spaces even for complex in vivo composite noise without additional regularization or failure modes."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A2A achieves one-shot ultrasound denoising via pyramid self-contrastive learning on sub-aperture signals to disentangle anatomy from noise, yielding large SNR and CNR gains in simulations and in vivo scans."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Self-contrastive learning on sub-aperture signals produces denoised ultrasound images from a single test sample."}],"snapshot_sha256":"3b3ba93090d562b27ae53fa0464822e6f87a0e827a1df4550f37cf822863b362"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"5d11a1761b0028d439a9f913006ee873bd0727d434448a1b109670a5bec7f6d7"},"paper":{"abstract_excerpt":"The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit noise assumptions whose validity diminishes under composite noise conditions. Learning-based methods require massive labeled data and model parameters. These pre-defined and pre-trained manners entail an inevitable domain shift in complex in vivo environments, so they are limited to a specific noise type and often blur structural details. In this study, we propose a pure test-time training framework for one-shot ultrasound image denoising and apply","authors_text":"Bingze Dai, Jiajing Zhang, Wei-Ning Lee, Xi Zhang, Yue Xu","cross_cats":["cs.AI"],"headline":"Self-contrastive learning on sub-aperture signals produces denoised ultrasound images from a single test sample.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-12T09:27:21Z","title":"Pyramid Self-contrastive Learning Framework for Test-time Ultrasound Image Denoising"},"references":{"count":68,"internal_anchors":2,"resolved_work":68,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"J. A. Jensen, S. I. Nikolov, K. L. Gammelmark, M. H. Pedersen, Syntheticapertureultrasoundimaging,Ultrasonics44(2006)e5–e15","work_id":"2fc608b0-e044-4d2e-822f-5bf04f89b740","year":2006},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"C. Papadacci, M. Pernot, M. Couade, M. Fink, M. Tanter, High- contrastultrafastimagingoftheheart,IEEEtransactionsonultrason- ics, ferroelectrics, and frequency control 61 (2) (2014) 288–301","work_id":"be2f8727-66e2-41f2-9c0f-66339dbf1347","year":2014},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"K. Krissian, R. Kikinis, C.-F. Westin, K. Vosburgh, Speckle- constrained filtering of ultrasound images, in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05),","work_id":"3882f29b-6e3e-4cb1-b1e8-0f02422d6d04","year":2005},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"H. Xie, L. E. Pierce, F. T. Ulaby, Statistical properties of logarith- mically transformed speckle, IEEE transactions on geoscience and remote sensing 40 (3) (2002) 721–727","work_id":"0931a0c2-7ff5-4f4d-8ad0-a7f6853a7255","year":2002},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"T. Loupas, W. McDicken, P. Allan, Noise reduction in ultrasonic images by digital filtering, The British journal of radiology 60 (712) (1987) 389–392","work_id":"6f36b4c8-5ba9-4f7d-a3d5-275d499f4b8b","year":1987}],"snapshot_sha256":"ba988434bc4a3bb33dc9858cdccb4e6cbc56e7259c1fdce2b4c6c6f11e958d84"},"source":{"id":"2605.12567","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T20:57:03.150351Z","id":"c2c5ae81-eab7-4e6a-81ed-8e51e4b314f1","model_set":{"reader":"grok-4.3"},"one_line_summary":"A2A achieves one-shot ultrasound denoising via pyramid self-contrastive learning on sub-aperture signals to disentangle anatomy from noise, yielding large SNR and CNR gains in simulations and in vivo scans.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Self-contrastive learning on sub-aperture signals produces denoised ultrasound images from a single test sample.","strongest_claim":"Our Aperture-to-Aperture (A2A) framework disentangles anatomical similarity and noise randomness from shuffled sub-apertures through self-contrastive learning in pyramid latent spaces. The clean image is then decoded from the anatomy space, while discarding the noise space. Simulation experiments demonstrated an improvement of 69.3% SNR and 34.4% CNR by A2A. The in vivo results showed 84.8% SNR and 25.7% CNR gains using only two aperture data.","weakest_assumption":"That sub-aperture signals in synthetic aperture ultrasound share sufficient anatomical content while differing primarily in independent noise realizations, allowing reliable disentanglement via self-contrastive learning in pyramid spaces even for complex in vivo composite noise without additional regularization or failure modes."}},"verdict_id":"c2c5ae81-eab7-4e6a-81ed-8e51e4b314f1"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9b196d99a57d90b3a23ff7ab739edf5a5103c8457cc0690479d554c3dd811919","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":"7650691334ac4d0d8bbdb821da603a4adf9df0ff0fff7d60c1a3e5c011fd3dc4","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-12T09:27:21Z","title_canon_sha256":"77c5da0b3ac853cbe93fa364ade39354eea4e48b2d4cbb2f28553185be13919a"},"schema_version":"1.0","source":{"id":"2605.12567","kind":"arxiv","version":1}},"canonical_sha256":"0d6027502c8cf9a92fc63a42a2b66a20c99ce5696ed241011060229238e4a4b8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0d6027502c8cf9a92fc63a42a2b66a20c99ce5696ed241011060229238e4a4b8","first_computed_at":"2026-05-18T03:10:01.839807Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:10:01.839807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S28HiUvlp3F2q8xPP6OA7+vQAs0DvTbnxnBIKTucLPuj0EYrH2dqofZ34VFX8evVJV49mxQpFrLtoQBVycnGAg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:10:01.840502Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12567","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9b196d99a57d90b3a23ff7ab739edf5a5103c8457cc0690479d554c3dd811919","sha256:74eb960cff75d589827766bbdbb2e3a3428af92994944150e47f9b8324aefea8"],"state_sha256":"c52a46a6f102afcec35fc48b6fc0c88f17208ecd8ea852b3edbdc74f98163e91"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jL/4162BIDX1NqZnzEwxmEE5Lq64+XK+H/S6DmfZa5oktCWjsO/Gje+7rnuQUPU8R4iayyGZNUgNnzreAR0ICw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-19T17:06:10.482757Z","bundle_sha256":"7346530fe6e6c61f2ef121fb9557e95da306f89b48271d3b86dc3e6fd2fcd876"}}