{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:HKKJZXQPITLBZDDLSYNTWKV5UH","short_pith_number":"pith:HKKJZXQP","canonical_record":{"source":{"id":"2605.05474","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-06T21:53:00Z","cross_cats_sorted":[],"title_canon_sha256":"46f093d2b50bddfd7588327bf70796d39cf63d1f5c00707acdd0af24b79a1fa5","abstract_canon_sha256":"7a277575c937aa5d987c268f60b4077a94c37c7f4d6054721403e1d1911ebc11"},"schema_version":"1.0"},"canonical_sha256":"3a949cde0f44d61c8c6b961b3b2abda1cf899783000de2f74ba05fe5b7d6749c","source":{"kind":"arxiv","id":"2605.05474","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.05474","created_at":"2026-06-08T01:04:06Z"},{"alias_kind":"arxiv_version","alias_value":"2605.05474v1","created_at":"2026-06-08T01:04:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.05474","created_at":"2026-06-08T01:04:06Z"},{"alias_kind":"pith_short_12","alias_value":"HKKJZXQPITLB","created_at":"2026-06-08T01:04:06Z"},{"alias_kind":"pith_short_16","alias_value":"HKKJZXQPITLBZDDL","created_at":"2026-06-08T01:04:06Z"},{"alias_kind":"pith_short_8","alias_value":"HKKJZXQP","created_at":"2026-06-08T01:04:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:HKKJZXQPITLBZDDLSYNTWKV5UH","target":"record","payload":{"canonical_record":{"source":{"id":"2605.05474","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-06T21:53:00Z","cross_cats_sorted":[],"title_canon_sha256":"46f093d2b50bddfd7588327bf70796d39cf63d1f5c00707acdd0af24b79a1fa5","abstract_canon_sha256":"7a277575c937aa5d987c268f60b4077a94c37c7f4d6054721403e1d1911ebc11"},"schema_version":"1.0"},"canonical_sha256":"3a949cde0f44d61c8c6b961b3b2abda1cf899783000de2f74ba05fe5b7d6749c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:04:06.671750Z","signature_b64":"Ef4/Pft2jjuOqmNmJ7Gwg8g/rSQA+YQW9cSUum6h4biaOBdjb6x/CgV1ZtskeaWtGvy1uQ1ZzcvzgBrXuzrOAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a949cde0f44d61c8c6b961b3b2abda1cf899783000de2f74ba05fe5b7d6749c","last_reissued_at":"2026-06-08T01:04:06.670868Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:04:06.670868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.05474","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":"2026-06-08T01:04:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pK1EyCxGdcFDSfde9m+v5ZZIGbR0areCAYHo8vCKVeVJZEDSf+wyGofh/esc4bdZLYM7vdFlSSWKoIKuWCYWDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T12:07:11.247816Z"},"content_sha256":"d76e97b93bb406dcb01eb2897b10dabc65978d6e5b7446d250c8b828d8569c44","schema_version":"1.0","event_id":"sha256:d76e97b93bb406dcb01eb2897b10dabc65978d6e5b7446d250c8b828d8569c44"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:HKKJZXQPITLBZDDLSYNTWKV5UH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Algorithm for Collaborative Optimization with Application to Aircraft Design","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A Bayesian algorithm for collaborative optimization uses Gaussian process surrogates to reduce black-box evaluations while achieving better designs in multidisciplinary problems.","cross_cats":[],"primary_cat":"math.OC","authors_text":"Mohamed Ali Belhafnaoui, Youssef Diouane","submitted_at":"2026-05-06T21:53:00Z","abstract_excerpt":"Collaborative Optimization (CO) is a multidisciplinary design optimization (MDO) framework that decomposes large-scale engineering problems into parallel, independently solvable subsystems coordinated by a system-level optimizer. Its practical utility is limited by the high frequency of expensive black-box disciplinary evaluations arising from the bi-level consistency constraints. This paper introduces BACO, a Bayesian Algorithm for Collaborative Optimization, which replaces the direct black-box calls at both levels with Gaussian process (GP) surrogates and acquisition function maximization. A"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"On the Scalable MDO problem, BACO consistently achieves lower objective values and drives both constraint violation and interdisciplinary discrepancy to near-zero within the evaluation budget, outperforming all three CO variants across all tested DoE sizes. On the CRM wing problem, BACO identifies a feasible solution within 886 of 1000 allocated evaluations.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The Gaussian process surrogates accurately capture the black-box disciplinary responses and feasibility regions sufficiently well that the acquisition-function-driven points remain informative and the predicted discrepancy constraints enforce true consistency.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"BACO replaces direct black-box calls in collaborative optimization with Gaussian process surrogates at both subsystem and system levels, achieving lower objectives and near-zero constraint violations on MDO benchmarks and a CRM wing problem within limited evaluations.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A Bayesian algorithm for collaborative optimization uses Gaussian process surrogates to reduce black-box evaluations while achieving better designs in multidisciplinary problems.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"a9c41ed0073e04ccee9a280a69f3d0d838eeac2ef01ac8a53a57a3042e741c58"},"source":{"id":"2605.05474","kind":"arxiv","version":1},"verdict":{"id":"e411448a-e90e-473a-b0cf-f6d476e70003","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T15:59:35.027159Z","strongest_claim":"On the Scalable MDO problem, BACO consistently achieves lower objective values and drives both constraint violation and interdisciplinary discrepancy to near-zero within the evaluation budget, outperforming all three CO variants across all tested DoE sizes. On the CRM wing problem, BACO identifies a feasible solution within 886 of 1000 allocated evaluations.","one_line_summary":"BACO replaces direct black-box calls in collaborative optimization with Gaussian process surrogates at both subsystem and system levels, achieving lower objectives and near-zero constraint violations on MDO benchmarks and a CRM wing problem within limited evaluations.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The Gaussian process surrogates accurately capture the black-box disciplinary responses and feasibility regions sufficiently well that the acquisition-function-driven points remain informative and the predicted discrepancy constraints enforce true consistency.","pith_extraction_headline":"A Bayesian algorithm for collaborative optimization uses Gaussian process surrogates to reduce black-box evaluations while achieving better designs in multidisciplinary problems."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.05474/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T09:40:59.421635Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T20:31:19.623699Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T13:31:15.987288Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"458acbdb77fa7929c6f8af91751d9bc61e401c66c68ec4b0e96d5fc387166640"},"references":{"count":42,"sample":[{"doi":"10.2514/1.j051895","year":2013,"title":"Multidisciplinary Design Optimization: A Survey of Architectures","work_id":"4c07829a-4acd-495e-a9c5-9c37242a2f8f","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1996,"title":"Collaborative Optimization: An Architecture for Large-Scale Distributed Design","work_id":"750c3000-0f72-4763-b230-0afa1e458a3e","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1995,"title":"Development and Application of the Collaborative Optimization Architecture in a Multidisci- plinary Design Environment","work_id":"47fcc023-0145-4470-8828-ea623e2382f9","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.2514/2.1646","year":2002,"title":"Analytical and Computational Aspects of Collaborative Optimization for Multidisci- plinary Design","work_id":"507e35c8-4024-40dd-af47-1b5391913119","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.2514/6.2008-","year":2008,"title":"Enhanced Collaborative Optimization: Application to an Analytic Test Problem and Aircraft Design","work_id":"9841927e-b522-4083-b7e5-04e1fa5af53f","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":42,"snapshot_sha256":"07e5974379a399a716376002d3c4c7b6e541711eb59fd595242fd4026171faa9","internal_anchors":1},"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":"e411448a-e90e-473a-b0cf-f6d476e70003"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-08T01:04:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h4we3FOs8GkueQ6S5sWnBZzP+RPCV34PnTpGSGAB/Cx/TUUF1FNTMThI9HqNy/OOudZoojKTDmiK874j0KcmDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T12:07:11.248402Z"},"content_sha256":"a46b615d34f940b48c4db10eb49f9ec5adc9185ba41f2f9ef4ba2d9198480673","schema_version":"1.0","event_id":"sha256:a46b615d34f940b48c4db10eb49f9ec5adc9185ba41f2f9ef4ba2d9198480673"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HKKJZXQPITLBZDDLSYNTWKV5UH/bundle.json","state_url":"https://pith.science/pith/HKKJZXQPITLBZDDLSYNTWKV5UH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HKKJZXQPITLBZDDLSYNTWKV5UH/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":"2026-06-30T12:07:11Z","links":{"resolver":"https://pith.science/pith/HKKJZXQPITLBZDDLSYNTWKV5UH","bundle":"https://pith.science/pith/HKKJZXQPITLBZDDLSYNTWKV5UH/bundle.json","state":"https://pith.science/pith/HKKJZXQPITLBZDDLSYNTWKV5UH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HKKJZXQPITLBZDDLSYNTWKV5UH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HKKJZXQPITLBZDDLSYNTWKV5UH","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":"7a277575c937aa5d987c268f60b4077a94c37c7f4d6054721403e1d1911ebc11","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-06T21:53:00Z","title_canon_sha256":"46f093d2b50bddfd7588327bf70796d39cf63d1f5c00707acdd0af24b79a1fa5"},"schema_version":"1.0","source":{"id":"2605.05474","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.05474","created_at":"2026-06-08T01:04:06Z"},{"alias_kind":"arxiv_version","alias_value":"2605.05474v1","created_at":"2026-06-08T01:04:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.05474","created_at":"2026-06-08T01:04:06Z"},{"alias_kind":"pith_short_12","alias_value":"HKKJZXQPITLB","created_at":"2026-06-08T01:04:06Z"},{"alias_kind":"pith_short_16","alias_value":"HKKJZXQPITLBZDDL","created_at":"2026-06-08T01:04:06Z"},{"alias_kind":"pith_short_8","alias_value":"HKKJZXQP","created_at":"2026-06-08T01:04:06Z"}],"graph_snapshots":[{"event_id":"sha256:a46b615d34f940b48c4db10eb49f9ec5adc9185ba41f2f9ef4ba2d9198480673","target":"graph","created_at":"2026-06-08T01:04:06Z","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":"On the Scalable MDO problem, BACO consistently achieves lower objective values and drives both constraint violation and interdisciplinary discrepancy to near-zero within the evaluation budget, outperforming all three CO variants across all tested DoE sizes. On the CRM wing problem, BACO identifies a feasible solution within 886 of 1000 allocated evaluations."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The Gaussian process surrogates accurately capture the black-box disciplinary responses and feasibility regions sufficiently well that the acquisition-function-driven points remain informative and the predicted discrepancy constraints enforce true consistency."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"BACO replaces direct black-box calls in collaborative optimization with Gaussian process surrogates at both subsystem and system levels, achieving lower objectives and near-zero constraint violations on MDO benchmarks and a CRM wing problem within limited evaluations."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A Bayesian algorithm for collaborative optimization uses Gaussian process surrogates to reduce black-box evaluations while achieving better designs in multidisciplinary problems."}],"snapshot_sha256":"a9c41ed0073e04ccee9a280a69f3d0d838eeac2ef01ac8a53a57a3042e741c58"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-20T09:40:59.421635Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T20:31:19.623699Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T13:31:15.987288Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.05474/integrity.json","findings":[],"snapshot_sha256":"458acbdb77fa7929c6f8af91751d9bc61e401c66c68ec4b0e96d5fc387166640","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Collaborative Optimization (CO) is a multidisciplinary design optimization (MDO) framework that decomposes large-scale engineering problems into parallel, independently solvable subsystems coordinated by a system-level optimizer. Its practical utility is limited by the high frequency of expensive black-box disciplinary evaluations arising from the bi-level consistency constraints. This paper introduces BACO, a Bayesian Algorithm for Collaborative Optimization, which replaces the direct black-box calls at both levels with Gaussian process (GP) surrogates and acquisition function maximization. A","authors_text":"Mohamed Ali Belhafnaoui, Youssef Diouane","cross_cats":[],"headline":"A Bayesian algorithm for collaborative optimization uses Gaussian process surrogates to reduce black-box evaluations while achieving better designs in multidisciplinary problems.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-06T21:53:00Z","title":"Bayesian Algorithm for Collaborative Optimization with Application to Aircraft Design"},"references":{"count":42,"internal_anchors":1,"resolved_work":42,"sample":[{"cited_arxiv_id":"","doi":"10.2514/1.j051895","is_internal_anchor":false,"ref_index":1,"title":"Multidisciplinary Design Optimization: A Survey of Architectures","work_id":"4c07829a-4acd-495e-a9c5-9c37242a2f8f","year":2013},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Collaborative Optimization: An Architecture for Large-Scale Distributed Design","work_id":"750c3000-0f72-4763-b230-0afa1e458a3e","year":1996},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Development and Application of the Collaborative Optimization Architecture in a Multidisci- plinary Design Environment","work_id":"47fcc023-0145-4470-8828-ea623e2382f9","year":1995},{"cited_arxiv_id":"","doi":"10.2514/2.1646","is_internal_anchor":false,"ref_index":4,"title":"Analytical and Computational Aspects of Collaborative Optimization for Multidisci- plinary Design","work_id":"507e35c8-4024-40dd-af47-1b5391913119","year":2002},{"cited_arxiv_id":"","doi":"10.2514/6.2008-","is_internal_anchor":false,"ref_index":5,"title":"Enhanced Collaborative Optimization: Application to an Analytic Test Problem and Aircraft Design","work_id":"9841927e-b522-4083-b7e5-04e1fa5af53f","year":2008}],"snapshot_sha256":"07e5974379a399a716376002d3c4c7b6e541711eb59fd595242fd4026171faa9"},"source":{"id":"2605.05474","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-08T15:59:35.027159Z","id":"e411448a-e90e-473a-b0cf-f6d476e70003","model_set":{"reader":"grok-4.3"},"one_line_summary":"BACO replaces direct black-box calls in collaborative optimization with Gaussian process surrogates at both subsystem and system levels, achieving lower objectives and near-zero constraint violations on MDO benchmarks and a CRM wing problem within limited evaluations.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A Bayesian algorithm for collaborative optimization uses Gaussian process surrogates to reduce black-box evaluations while achieving better designs in multidisciplinary problems.","strongest_claim":"On the Scalable MDO problem, BACO consistently achieves lower objective values and drives both constraint violation and interdisciplinary discrepancy to near-zero within the evaluation budget, outperforming all three CO variants across all tested DoE sizes. On the CRM wing problem, BACO identifies a feasible solution within 886 of 1000 allocated evaluations.","weakest_assumption":"The Gaussian process surrogates accurately capture the black-box disciplinary responses and feasibility regions sufficiently well that the acquisition-function-driven points remain informative and the predicted discrepancy constraints enforce true consistency."}},"verdict_id":"e411448a-e90e-473a-b0cf-f6d476e70003"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d76e97b93bb406dcb01eb2897b10dabc65978d6e5b7446d250c8b828d8569c44","target":"record","created_at":"2026-06-08T01:04:06Z","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":"7a277575c937aa5d987c268f60b4077a94c37c7f4d6054721403e1d1911ebc11","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-06T21:53:00Z","title_canon_sha256":"46f093d2b50bddfd7588327bf70796d39cf63d1f5c00707acdd0af24b79a1fa5"},"schema_version":"1.0","source":{"id":"2605.05474","kind":"arxiv","version":1}},"canonical_sha256":"3a949cde0f44d61c8c6b961b3b2abda1cf899783000de2f74ba05fe5b7d6749c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3a949cde0f44d61c8c6b961b3b2abda1cf899783000de2f74ba05fe5b7d6749c","first_computed_at":"2026-06-08T01:04:06.670868Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:04:06.670868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ef4/Pft2jjuOqmNmJ7Gwg8g/rSQA+YQW9cSUum6h4biaOBdjb6x/CgV1ZtskeaWtGvy1uQ1ZzcvzgBrXuzrOAw==","signature_status":"signed_v1","signed_at":"2026-06-08T01:04:06.671750Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.05474","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d76e97b93bb406dcb01eb2897b10dabc65978d6e5b7446d250c8b828d8569c44","sha256:a46b615d34f940b48c4db10eb49f9ec5adc9185ba41f2f9ef4ba2d9198480673"],"state_sha256":"32128906203371f7de069240225aa911d520bae9f4d5cf857a082272424b8a6b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jNuwg4UcSwZ5nt+MHXRaMfuqsPt4lHpcpk07nvYfTaeoaJWvogUL4sOqBDzT927IS0ZJO5AMq/bTVza89YA2Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T12:07:11.250832Z","bundle_sha256":"3bca639d4ff8bdd6c12e59c2b53df3639e600a242868a111fd7b4ed16cefe6af"}}