{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:Y3O4FLBNWA5FX745ZTVBD5TQQ3","short_pith_number":"pith:Y3O4FLBN","canonical_record":{"source":{"id":"2604.13662","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mes-hall","submitted_at":"2026-04-15T09:29:24Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"1aae7d822c97c30feee94e3a99f4fcf316fa2378b7f837cb412c9ac4f1c1ebf0","abstract_canon_sha256":"6dde6be0ef8571dda6ec0667f0f8b527f6b1aa402cbc5c48b906ec1887766d94"},"schema_version":"1.0"},"canonical_sha256":"c6ddc2ac2db03a5bff9dccea11f67086e53a68b48f2708c5680026facfc36fe5","source":{"kind":"arxiv","id":"2604.13662","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.13662","created_at":"2026-06-19T16:10:37Z"},{"alias_kind":"arxiv_version","alias_value":"2604.13662v1","created_at":"2026-06-19T16:10:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.13662","created_at":"2026-06-19T16:10:37Z"},{"alias_kind":"pith_short_12","alias_value":"Y3O4FLBNWA5F","created_at":"2026-06-19T16:10:37Z"},{"alias_kind":"pith_short_16","alias_value":"Y3O4FLBNWA5FX745","created_at":"2026-06-19T16:10:37Z"},{"alias_kind":"pith_short_8","alias_value":"Y3O4FLBN","created_at":"2026-06-19T16:10:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:Y3O4FLBNWA5FX745ZTVBD5TQQ3","target":"record","payload":{"canonical_record":{"source":{"id":"2604.13662","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mes-hall","submitted_at":"2026-04-15T09:29:24Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"1aae7d822c97c30feee94e3a99f4fcf316fa2378b7f837cb412c9ac4f1c1ebf0","abstract_canon_sha256":"6dde6be0ef8571dda6ec0667f0f8b527f6b1aa402cbc5c48b906ec1887766d94"},"schema_version":"1.0"},"canonical_sha256":"c6ddc2ac2db03a5bff9dccea11f67086e53a68b48f2708c5680026facfc36fe5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:37.441409Z","signature_b64":"/QWrciNocvTX0Q0gP9rTixAdsocZTBNNfhaPLRk8UWQyjvDxIdnrliwHQ8PyAQe7oYKfL3Co8rKSQt7XdE5uAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6ddc2ac2db03a5bff9dccea11f67086e53a68b48f2708c5680026facfc36fe5","last_reissued_at":"2026-06-19T16:10:37.440845Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:37.440845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.13662","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":"2026-06-19T16:10:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"md1+z86HW9v3m189lCeH+F+FuyzO21EHTYyiha1XhbG4MyhFqB8BQah22bgng1u44vxAG63xUw6HwShC3RQdDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T00:34:09.178722Z"},"content_sha256":"ce854989966be91e677bc9877a21264d1a99d3f6a66170c79d81df0845d85d1a","schema_version":"1.0","event_id":"sha256:ce854989966be91e677bc9877a21264d1a99d3f6a66170c79d81df0845d85d1a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:Y3O4FLBNWA5FX745ZTVBD5TQQ3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatic Charge State Tuning of 300 mm FDSOI Quantum Dots Using Neural Network Segmentation of Charge Stability Diagram","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A neural network segments charge stability diagrams to auto-tune silicon quantum dots to the single-charge regime with 80% success.","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cond-mat.mes-hall","authors_text":"Amine Torki, Emmanuel Chanrion, Peter Samaha, Pierre-Andre Mortemousque, Sam Fiette, Yann Beilliard, Ysaline Renaud","submitted_at":"2026-04-15T09:29:24Z","abstract_excerpt":"Tuning of gate-defined semiconductor quantum dots (QDs) is a major bottleneck for scaling spin qubit technologies. We present a deep learning (DL) driven, semantic-segmentation pipeline that performs charge auto-tuning by locating transition lines in full charge stability diagrams (CSDs) and returns gate voltage targets for the single charge regime. We assemble and manually annotate a large, heterogeneous dataset of 1015 experimental CSDs measured from silicon QD devices, spanning nine design geometries, multiple wafers, and fabrication runs. A U-Net style convolutional neural network (CNN) wi"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our model achieves an overall offline tuning success of 80.0% in locating the single-charge regime, with peak performance exceeding 88% for some designs.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The manually annotated dataset of 1015 CSDs from nine geometries is representative of future devices and that successful segmentation of transition lines directly corresponds to correct physical identification of the single-electron regime without systematic false positives on unseen wafers.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A U-Net CNN segments experimental charge stability diagrams to locate the single-charge regime in 300 mm FDSOI quantum dots with 80% overall success and up to 88% on some designs.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A neural network segments charge stability diagrams to auto-tune silicon quantum dots to the single-charge regime with 80% success.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"5bb1c56d55cad3b9da8fc15827002981dbb7ecc79f32600d0fd1c741db224087"},"source":{"id":"2604.13662","kind":"arxiv","version":1},"verdict":{"id":"0d0a426c-d068-4dfc-b98c-df01cbbd95ec","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T12:58:00.642621Z","strongest_claim":"Our model achieves an overall offline tuning success of 80.0% in locating the single-charge regime, with peak performance exceeding 88% for some designs.","one_line_summary":"A U-Net CNN segments experimental charge stability diagrams to locate the single-charge regime in 300 mm FDSOI quantum dots with 80% overall success and up to 88% on some designs.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The manually annotated dataset of 1015 CSDs from nine geometries is representative of future devices and that successful segmentation of transition lines directly corresponds to correct physical identification of the single-electron regime without systematic false positives on unseen wafers.","pith_extraction_headline":"A neural network segments charge stability diagrams to auto-tune silicon quantum dots to the single-charge regime with 80% success."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.13662/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":57,"sample":[{"doi":"","year":null,"title":"Data Acquisition Energy Cryostat Automatic detection of 1e- regime using stability diagram segmentation Trained U-Net model Predicted maskStability diagram real-time data flow offline data flow Datase","work_id":"0cc5ae6f-ae4a-4e88-8a1d-0d2eaa5bfcf0","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Inference pre-processing post-processing 1e- regime MobileNetV2 custom decoder SET Qubit FIG. 1. Schematic summary of the offline auto-tuning pipeline. T op (Data acquisition): experimental setup and ","work_id":"9b468c81-1c80-4ae8-9312-88c1d618a893","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Single QD-SET","work_id":"82899e20-96e6-47a1-a7a3-d5ddf7e55773","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Model training2. Data annotation Normalize Load annotated dataset Evaluate performance Obtain dataset of 1015 labeled samples Perform 5-fold training Load stored CSDs Filter out irrelevant* CSDs Annot","work_id":"d55dece4-554d-4a30-a5bb-3c10167712b9","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Inference Load one CSD Trained U-Net model Threshold & binarize Skeletonize Normalize Compute region centroid Output gate voltages (VQD, VSET) Extract region between first two transition lines Morphol","work_id":"530b883d-9c5d-4515-86cf-a3941bf8f83b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":57,"snapshot_sha256":"2290aac80c661bb59200316be2c7b143c903dbf708d47b067db2c8c043719852","internal_anchors":2},"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":"0d0a426c-d068-4dfc-b98c-df01cbbd95ec"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-19T16:10:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d2zBtJ3c97MC/AezolMzmnTRxYUMVf3C5+gBAliRBVQjQn0qc+74LAAijBkUy4SJk54G91t5UK06xElohMnHAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T00:34:09.179526Z"},"content_sha256":"d03073c1004a6530135600ac48700db321c07650a428f869d840b78cb00f486e","schema_version":"1.0","event_id":"sha256:d03073c1004a6530135600ac48700db321c07650a428f869d840b78cb00f486e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y3O4FLBNWA5FX745ZTVBD5TQQ3/bundle.json","state_url":"https://pith.science/pith/Y3O4FLBNWA5FX745ZTVBD5TQQ3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y3O4FLBNWA5FX745ZTVBD5TQQ3/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":"2026-08-16T00:34:09Z","links":{"resolver":"https://pith.science/pith/Y3O4FLBNWA5FX745ZTVBD5TQQ3","bundle":"https://pith.science/pith/Y3O4FLBNWA5FX745ZTVBD5TQQ3/bundle.json","state":"https://pith.science/pith/Y3O4FLBNWA5FX745ZTVBD5TQQ3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y3O4FLBNWA5FX745ZTVBD5TQQ3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:Y3O4FLBNWA5FX745ZTVBD5TQQ3","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":"6dde6be0ef8571dda6ec0667f0f8b527f6b1aa402cbc5c48b906ec1887766d94","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mes-hall","submitted_at":"2026-04-15T09:29:24Z","title_canon_sha256":"1aae7d822c97c30feee94e3a99f4fcf316fa2378b7f837cb412c9ac4f1c1ebf0"},"schema_version":"1.0","source":{"id":"2604.13662","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.13662","created_at":"2026-06-19T16:10:37Z"},{"alias_kind":"arxiv_version","alias_value":"2604.13662v1","created_at":"2026-06-19T16:10:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.13662","created_at":"2026-06-19T16:10:37Z"},{"alias_kind":"pith_short_12","alias_value":"Y3O4FLBNWA5F","created_at":"2026-06-19T16:10:37Z"},{"alias_kind":"pith_short_16","alias_value":"Y3O4FLBNWA5FX745","created_at":"2026-06-19T16:10:37Z"},{"alias_kind":"pith_short_8","alias_value":"Y3O4FLBN","created_at":"2026-06-19T16:10:37Z"}],"graph_snapshots":[{"event_id":"sha256:d03073c1004a6530135600ac48700db321c07650a428f869d840b78cb00f486e","target":"graph","created_at":"2026-06-19T16:10:37Z","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":"Our model achieves an overall offline tuning success of 80.0% in locating the single-charge regime, with peak performance exceeding 88% for some designs."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The manually annotated dataset of 1015 CSDs from nine geometries is representative of future devices and that successful segmentation of transition lines directly corresponds to correct physical identification of the single-electron regime without systematic false positives on unseen wafers."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A U-Net CNN segments experimental charge stability diagrams to locate the single-charge regime in 300 mm FDSOI quantum dots with 80% overall success and up to 88% on some designs."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A neural network segments charge stability diagrams to auto-tune silicon quantum dots to the single-charge regime with 80% success."}],"snapshot_sha256":"5bb1c56d55cad3b9da8fc15827002981dbb7ecc79f32600d0fd1c741db224087"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.13662/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Tuning of gate-defined semiconductor quantum dots (QDs) is a major bottleneck for scaling spin qubit technologies. We present a deep learning (DL) driven, semantic-segmentation pipeline that performs charge auto-tuning by locating transition lines in full charge stability diagrams (CSDs) and returns gate voltage targets for the single charge regime. We assemble and manually annotate a large, heterogeneous dataset of 1015 experimental CSDs measured from silicon QD devices, spanning nine design geometries, multiple wafers, and fabrication runs. A U-Net style convolutional neural network (CNN) wi","authors_text":"Amine Torki, Emmanuel Chanrion, Peter Samaha, Pierre-Andre Mortemousque, Sam Fiette, Yann Beilliard, Ysaline Renaud","cross_cats":["cs.CV","cs.LG"],"headline":"A neural network segments charge stability diagrams to auto-tune silicon quantum dots to the single-charge regime with 80% success.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mes-hall","submitted_at":"2026-04-15T09:29:24Z","title":"Automatic Charge State Tuning of 300 mm FDSOI Quantum Dots Using Neural Network Segmentation of Charge Stability Diagram"},"references":{"count":57,"internal_anchors":2,"resolved_work":57,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Data Acquisition Energy Cryostat Automatic detection of 1e- regime using stability diagram segmentation Trained U-Net model Predicted maskStability diagram real-time data flow offline data flow Datase","work_id":"0cc5ae6f-ae4a-4e88-8a1d-0d2eaa5bfcf0","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Inference pre-processing post-processing 1e- regime MobileNetV2 custom decoder SET Qubit FIG. 1. Schematic summary of the offline auto-tuning pipeline. T op (Data acquisition): experimental setup and ","work_id":"9b468c81-1c80-4ae8-9312-88c1d618a893","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Single QD-SET","work_id":"82899e20-96e6-47a1-a7a3-d5ddf7e55773","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Model training2. Data annotation Normalize Load annotated dataset Evaluate performance Obtain dataset of 1015 labeled samples Perform 5-fold training Load stored CSDs Filter out irrelevant* CSDs Annot","work_id":"d55dece4-554d-4a30-a5bb-3c10167712b9","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Inference Load one CSD Trained U-Net model Threshold & binarize Skeletonize Normalize Compute region centroid Output gate voltages (VQD, VSET) Extract region between first two transition lines Morphol","work_id":"530b883d-9c5d-4515-86cf-a3941bf8f83b","year":null}],"snapshot_sha256":"2290aac80c661bb59200316be2c7b143c903dbf708d47b067db2c8c043719852"},"source":{"id":"2604.13662","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-10T12:58:00.642621Z","id":"0d0a426c-d068-4dfc-b98c-df01cbbd95ec","model_set":{"reader":"grok-4.3"},"one_line_summary":"A U-Net CNN segments experimental charge stability diagrams to locate the single-charge regime in 300 mm FDSOI quantum dots with 80% overall success and up to 88% on some designs.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A neural network segments charge stability diagrams to auto-tune silicon quantum dots to the single-charge regime with 80% success.","strongest_claim":"Our model achieves an overall offline tuning success of 80.0% in locating the single-charge regime, with peak performance exceeding 88% for some designs.","weakest_assumption":"The manually annotated dataset of 1015 CSDs from nine geometries is representative of future devices and that successful segmentation of transition lines directly corresponds to correct physical identification of the single-electron regime without systematic false positives on unseen wafers."}},"verdict_id":"0d0a426c-d068-4dfc-b98c-df01cbbd95ec"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ce854989966be91e677bc9877a21264d1a99d3f6a66170c79d81df0845d85d1a","target":"record","created_at":"2026-06-19T16:10:37Z","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":"6dde6be0ef8571dda6ec0667f0f8b527f6b1aa402cbc5c48b906ec1887766d94","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mes-hall","submitted_at":"2026-04-15T09:29:24Z","title_canon_sha256":"1aae7d822c97c30feee94e3a99f4fcf316fa2378b7f837cb412c9ac4f1c1ebf0"},"schema_version":"1.0","source":{"id":"2604.13662","kind":"arxiv","version":1}},"canonical_sha256":"c6ddc2ac2db03a5bff9dccea11f67086e53a68b48f2708c5680026facfc36fe5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c6ddc2ac2db03a5bff9dccea11f67086e53a68b48f2708c5680026facfc36fe5","first_computed_at":"2026-06-19T16:10:37.440845Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:10:37.440845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/QWrciNocvTX0Q0gP9rTixAdsocZTBNNfhaPLRk8UWQyjvDxIdnrliwHQ8PyAQe7oYKfL3Co8rKSQt7XdE5uAg==","signature_status":"signed_v1","signed_at":"2026-06-19T16:10:37.441409Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.13662","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ce854989966be91e677bc9877a21264d1a99d3f6a66170c79d81df0845d85d1a","sha256:d03073c1004a6530135600ac48700db321c07650a428f869d840b78cb00f486e"],"state_sha256":"d890d39fd1ac48fb4afc25f418758a0d25ed6e5c2f61c7cf31c8ddd285f57a57"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tnJFBdvBKIsbpiLc6E5hFvXg1IDuXZuTUMcPivpBtkCyl2J3ILpsnq2GdzIc+OWoYh20jGzkcAhFbdPOD8ecCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T00:34:09.185082Z","bundle_sha256":"d6cd9f98d62139ea378b4bc012d058904847f134bb1765ad1efae70d78f17ec6"}}