{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:VOH7P5BMHVBK7HCTR6B5FLOO4O","short_pith_number":"pith:VOH7P5BM","canonical_record":{"source":{"id":"2605.15350","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-14T19:20:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"609c2a39fee63bf4345ea361eb8c20988033413f11d7492d64d032bc2a1de815","abstract_canon_sha256":"4df34068a89c803490e19e2cc5fd4292d1ef0035f5329b947d65b1533053058c"},"schema_version":"1.0"},"canonical_sha256":"ab8ff7f42c3d42af9c538f83d2adcee38821c02839b848ee8eb0511545f584ac","source":{"kind":"arxiv","id":"2605.15350","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15350","created_at":"2026-05-20T00:00:53Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15350v1","created_at":"2026-05-20T00:00:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15350","created_at":"2026-05-20T00:00:53Z"},{"alias_kind":"pith_short_12","alias_value":"VOH7P5BMHVBK","created_at":"2026-05-20T00:00:53Z"},{"alias_kind":"pith_short_16","alias_value":"VOH7P5BMHVBK7HCT","created_at":"2026-05-20T00:00:53Z"},{"alias_kind":"pith_short_8","alias_value":"VOH7P5BM","created_at":"2026-05-20T00:00:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:VOH7P5BMHVBK7HCTR6B5FLOO4O","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15350","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-14T19:20:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"609c2a39fee63bf4345ea361eb8c20988033413f11d7492d64d032bc2a1de815","abstract_canon_sha256":"4df34068a89c803490e19e2cc5fd4292d1ef0035f5329b947d65b1533053058c"},"schema_version":"1.0"},"canonical_sha256":"ab8ff7f42c3d42af9c538f83d2adcee38821c02839b848ee8eb0511545f584ac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:00:53.837561Z","signature_b64":"E5NZ73ztf5nemsAwsvVMhmfGE1T94GIWqxfDgbf7uy6qE0x5QPW+SCG2PfPI6iUlg3kDo5BOR3IHrmk18YtuCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab8ff7f42c3d42af9c538f83d2adcee38821c02839b848ee8eb0511545f584ac","last_reissued_at":"2026-05-20T00:00:53.836646Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:00:53.836646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15350","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":"2026-05-20T00:00:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nVYaBooWc4lg+6Xt6ISogA14H3Hnwze++NDuBB4Rsx9T9BBzqOMaCos6IYeADi5T2sygg2yw70t9doHqiTrGAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:15:41.748615Z"},"content_sha256":"314c16cdea10ac97840c46dbf19163f5801cd9cb10ac3e781bf02bf0dec5a692","schema_version":"1.0","event_id":"sha256:314c16cdea10ac97840c46dbf19163f5801cd9cb10ac3e781bf02bf0dec5a692"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:VOH7P5BMHVBK7HCTR6B5FLOO4O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A hybrid momentum Frank-Wolfe algorithm achieves the optimal O(K^{-1/4}) convergence rate for stochastic compositional problems with merely Lipschitz outer functions.","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"El Mahdi Chayti","submitted_at":"2026-05-14T19:20:22Z","abstract_excerpt":"Stochastic compositional optimization minimizes objectives of the form $\\min_{\\bm{x} \\in \\mathcal{X}} F(\\bm{f}(\\bm{x}), \\bm{x})$, where $\\bm{f}$ is accessible only through noisy stochastic queries. Existing methods for this problem assume that the outer function $F$ is continuously differentiable, which excludes many practically important applications such as robust max-of-losses, Conditional Value-at-Risk, and norm regularizers. We propose the Hybrid Momentum Stochastic Frank--Wolfe algorithm, which drops the smoothness assumption on $F$. By combining a momentum-based Jacobian tracker with a "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We establish an O(K^{-1/4}) convergence rate in the generalized Frank-Wolfe gap for non-convex objectives with L_F-Lipschitz outer functions, matching the optimal complexity for projection-free single-sample stochastic methods under expected smoothness.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The outer function F is L_F-Lipschitz (but not necessarily differentiable), and the stochastic oracles satisfy expected smoothness or bounded r-th moments for r in (1,2]. This premise enters in the convergence analysis that bounds the generalized Frank-Wolfe gap.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"The Hybrid Momentum Stochastic Frank-Wolfe algorithm achieves O(K^{-1/4}) convergence in the generalized Frank-Wolfe gap for non-convex stochastic compositional optimization with Lipschitz outer functions.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A hybrid momentum Frank-Wolfe algorithm achieves the optimal O(K^{-1/4}) convergence rate for stochastic compositional problems with merely Lipschitz outer functions.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8ce409d1b17599da335ddb602a40b9f7d93d3a933105c4f71ea32188ab37594d"},"source":{"id":"2605.15350","kind":"arxiv","version":1},"verdict":{"id":"4f28231b-3d25-42ca-88ee-687410a79638","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T15:13:15.353263Z","strongest_claim":"We establish an O(K^{-1/4}) convergence rate in the generalized Frank-Wolfe gap for non-convex objectives with L_F-Lipschitz outer functions, matching the optimal complexity for projection-free single-sample stochastic methods under expected smoothness.","one_line_summary":"The Hybrid Momentum Stochastic Frank-Wolfe algorithm achieves O(K^{-1/4}) convergence in the generalized Frank-Wolfe gap for non-convex stochastic compositional optimization with Lipschitz outer functions.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The outer function F is L_F-Lipschitz (but not necessarily differentiable), and the stochastic oracles satisfy expected smoothness or bounded r-th moments for r in (1,2]. This premise enters in the convergence analysis that bounds the generalized Frank-Wolfe gap.","pith_extraction_headline":"A hybrid momentum Frank-Wolfe algorithm achieves the optimal O(K^{-1/4}) convergence rate for stochastic compositional problems with merely Lipschitz outer functions."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15350/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T15:31:17.897075Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T15:23:17.789332Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.203456Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.749798Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"235384f2c8d7af225b33db293f624ed2eb7929872640ee8cbf2c49a7958816fd"},"references":{"count":32,"sample":[{"doi":"","year":2023,"title":"Conference on Learning Theory , pages=","work_id":"24de1194-bb5e-47eb-8b1b-a040d5e2566c","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"2024 , url =","work_id":"c28d2d3e-2633-40c3-a165-53d276975c5f","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"IEEE Transactions on Signal Processing , volume=","work_id":"a95358f8-77ef-4467-aada-4aeb43748f8b","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Mathematical Programming , volume=","work_id":"644b16c4-500b-46ab-a84e-70145964882b","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Journal of Machine Learning Research , volume=","work_id":"aa7974ef-7ece-4a5e-af49-5643c7012368","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":32,"snapshot_sha256":"ea5f7c2b2495d6d74541803e5891a171dff3dd6dc8d55753e60a39f6bb868550","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"6bd20c45fbe31e473231adc5f5fda970335c5cbf588ba2132bdbe6ba8dd41b8f"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"4f28231b-3d25-42ca-88ee-687410a79638"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:00:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RuZh3u5RFHTfgRXN5tpSXI1GY9xor3fypO5zg0nSbY+15K9hTrvlDWbKIHOXah0wfOmwQmq3HFuQn00d+2wWAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:15:41.749809Z"},"content_sha256":"676c0d95b3734b4f871f8ced5b822e113651a6725466d1d00791b8f41786ccdf","schema_version":"1.0","event_id":"sha256:676c0d95b3734b4f871f8ced5b822e113651a6725466d1d00791b8f41786ccdf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VOH7P5BMHVBK7HCTR6B5FLOO4O/bundle.json","state_url":"https://pith.science/pith/VOH7P5BMHVBK7HCTR6B5FLOO4O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VOH7P5BMHVBK7HCTR6B5FLOO4O/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":"2026-05-25T20:15:41Z","links":{"resolver":"https://pith.science/pith/VOH7P5BMHVBK7HCTR6B5FLOO4O","bundle":"https://pith.science/pith/VOH7P5BMHVBK7HCTR6B5FLOO4O/bundle.json","state":"https://pith.science/pith/VOH7P5BMHVBK7HCTR6B5FLOO4O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VOH7P5BMHVBK7HCTR6B5FLOO4O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:VOH7P5BMHVBK7HCTR6B5FLOO4O","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":"4df34068a89c803490e19e2cc5fd4292d1ef0035f5329b947d65b1533053058c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-14T19:20:22Z","title_canon_sha256":"609c2a39fee63bf4345ea361eb8c20988033413f11d7492d64d032bc2a1de815"},"schema_version":"1.0","source":{"id":"2605.15350","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15350","created_at":"2026-05-20T00:00:53Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15350v1","created_at":"2026-05-20T00:00:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15350","created_at":"2026-05-20T00:00:53Z"},{"alias_kind":"pith_short_12","alias_value":"VOH7P5BMHVBK","created_at":"2026-05-20T00:00:53Z"},{"alias_kind":"pith_short_16","alias_value":"VOH7P5BMHVBK7HCT","created_at":"2026-05-20T00:00:53Z"},{"alias_kind":"pith_short_8","alias_value":"VOH7P5BM","created_at":"2026-05-20T00:00:53Z"}],"graph_snapshots":[{"event_id":"sha256:676c0d95b3734b4f871f8ced5b822e113651a6725466d1d00791b8f41786ccdf","target":"graph","created_at":"2026-05-20T00:00:53Z","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":"We establish an O(K^{-1/4}) convergence rate in the generalized Frank-Wolfe gap for non-convex objectives with L_F-Lipschitz outer functions, matching the optimal complexity for projection-free single-sample stochastic methods under expected smoothness."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The outer function F is L_F-Lipschitz (but not necessarily differentiable), and the stochastic oracles satisfy expected smoothness or bounded r-th moments for r in (1,2]. This premise enters in the convergence analysis that bounds the generalized Frank-Wolfe gap."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"The Hybrid Momentum Stochastic Frank-Wolfe algorithm achieves O(K^{-1/4}) convergence in the generalized Frank-Wolfe gap for non-convex stochastic compositional optimization with Lipschitz outer functions."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A hybrid momentum Frank-Wolfe algorithm achieves the optimal O(K^{-1/4}) convergence rate for stochastic compositional problems with merely Lipschitz outer functions."}],"snapshot_sha256":"8ce409d1b17599da335ddb602a40b9f7d93d3a933105c4f71ea32188ab37594d"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"6bd20c45fbe31e473231adc5f5fda970335c5cbf588ba2132bdbe6ba8dd41b8f"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T15:31:17.897075Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T15:23:17.789332Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.203456Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.749798Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.15350/integrity.json","findings":[],"snapshot_sha256":"235384f2c8d7af225b33db293f624ed2eb7929872640ee8cbf2c49a7958816fd","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Stochastic compositional optimization minimizes objectives of the form $\\min_{\\bm{x} \\in \\mathcal{X}} F(\\bm{f}(\\bm{x}), \\bm{x})$, where $\\bm{f}$ is accessible only through noisy stochastic queries. Existing methods for this problem assume that the outer function $F$ is continuously differentiable, which excludes many practically important applications such as robust max-of-losses, Conditional Value-at-Risk, and norm regularizers. We propose the Hybrid Momentum Stochastic Frank--Wolfe algorithm, which drops the smoothness assumption on $F$. By combining a momentum-based Jacobian tracker with a ","authors_text":"El Mahdi Chayti","cross_cats":["cs.LG"],"headline":"A hybrid momentum Frank-Wolfe algorithm achieves the optimal O(K^{-1/4}) convergence rate for stochastic compositional problems with merely Lipschitz outer functions.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-14T19:20:22Z","title":"Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe"},"references":{"count":32,"internal_anchors":0,"resolved_work":32,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Conference on Learning Theory , pages=","work_id":"24de1194-bb5e-47eb-8b1b-a040d5e2566c","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"2024 , url =","work_id":"c28d2d3e-2633-40c3-a165-53d276975c5f","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"IEEE Transactions on Signal Processing , volume=","work_id":"a95358f8-77ef-4467-aada-4aeb43748f8b","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Mathematical Programming , volume=","work_id":"644b16c4-500b-46ab-a84e-70145964882b","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Journal of Machine Learning Research , volume=","work_id":"aa7974ef-7ece-4a5e-af49-5643c7012368","year":null}],"snapshot_sha256":"ea5f7c2b2495d6d74541803e5891a171dff3dd6dc8d55753e60a39f6bb868550"},"source":{"id":"2605.15350","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T15:13:15.353263Z","id":"4f28231b-3d25-42ca-88ee-687410a79638","model_set":{"reader":"grok-4.3"},"one_line_summary":"The Hybrid Momentum Stochastic Frank-Wolfe algorithm achieves O(K^{-1/4}) convergence in the generalized Frank-Wolfe gap for non-convex stochastic compositional optimization with Lipschitz outer functions.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A hybrid momentum Frank-Wolfe algorithm achieves the optimal O(K^{-1/4}) convergence rate for stochastic compositional problems with merely Lipschitz outer functions.","strongest_claim":"We establish an O(K^{-1/4}) convergence rate in the generalized Frank-Wolfe gap for non-convex objectives with L_F-Lipschitz outer functions, matching the optimal complexity for projection-free single-sample stochastic methods under expected smoothness.","weakest_assumption":"The outer function F is L_F-Lipschitz (but not necessarily differentiable), and the stochastic oracles satisfy expected smoothness or bounded r-th moments for r in (1,2]. This premise enters in the convergence analysis that bounds the generalized Frank-Wolfe gap."}},"verdict_id":"4f28231b-3d25-42ca-88ee-687410a79638"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:314c16cdea10ac97840c46dbf19163f5801cd9cb10ac3e781bf02bf0dec5a692","target":"record","created_at":"2026-05-20T00:00:53Z","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":"4df34068a89c803490e19e2cc5fd4292d1ef0035f5329b947d65b1533053058c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-14T19:20:22Z","title_canon_sha256":"609c2a39fee63bf4345ea361eb8c20988033413f11d7492d64d032bc2a1de815"},"schema_version":"1.0","source":{"id":"2605.15350","kind":"arxiv","version":1}},"canonical_sha256":"ab8ff7f42c3d42af9c538f83d2adcee38821c02839b848ee8eb0511545f584ac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ab8ff7f42c3d42af9c538f83d2adcee38821c02839b848ee8eb0511545f584ac","first_computed_at":"2026-05-20T00:00:53.836646Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:00:53.836646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"E5NZ73ztf5nemsAwsvVMhmfGE1T94GIWqxfDgbf7uy6qE0x5QPW+SCG2PfPI6iUlg3kDo5BOR3IHrmk18YtuCQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:00:53.837561Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15350","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:314c16cdea10ac97840c46dbf19163f5801cd9cb10ac3e781bf02bf0dec5a692","sha256:676c0d95b3734b4f871f8ced5b822e113651a6725466d1d00791b8f41786ccdf"],"state_sha256":"0480de49390755055831f5f3fe43af7a8d6c93475f5345dccd70ef520cc05285"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PNMT78wVW0KSR21YI6QASGzR2qknWHaKYL0Bha0Eo9yeq/ddsHm/t25gVsc/3Y68xJB9HsABc0SYR5+IDXMrAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T20:15:41.755067Z","bundle_sha256":"44b237a6e4c821e68452f79f3b671b165c0a6ba172f5fe1fa25629624b0dc713"}}