{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:S4UISSUIKYF6QDZW4C7OPCRKOG","short_pith_number":"pith:S4UISSUI","canonical_record":{"source":{"id":"2605.15301","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-14T18:15:09Z","cross_cats_sorted":[],"title_canon_sha256":"d34b07cc836d56f8d823a4b728f7102f266f80326af7d78f3c2fd356132cf115","abstract_canon_sha256":"ef14b2524be4c23e7e7f0bfc293f5059018f0744526972431bcea2a3a3c7e19e"},"schema_version":"1.0"},"canonical_sha256":"9728894a88560be80f36e0bee78a2a71b274dce595a03a03dce790586a083fdb","source":{"kind":"arxiv","id":"2605.15301","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15301","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15301v1","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15301","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_12","alias_value":"S4UISSUIKYF6","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_16","alias_value":"S4UISSUIKYF6QDZW","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_8","alias_value":"S4UISSUI","created_at":"2026-05-20T00:00:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:S4UISSUIKYF6QDZW4C7OPCRKOG","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15301","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-14T18:15:09Z","cross_cats_sorted":[],"title_canon_sha256":"d34b07cc836d56f8d823a4b728f7102f266f80326af7d78f3c2fd356132cf115","abstract_canon_sha256":"ef14b2524be4c23e7e7f0bfc293f5059018f0744526972431bcea2a3a3c7e19e"},"schema_version":"1.0"},"canonical_sha256":"9728894a88560be80f36e0bee78a2a71b274dce595a03a03dce790586a083fdb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:00:51.610963Z","signature_b64":"XLnpnkTqDah9pGci8CUUOVGwOqzkphZPBIAXLp1mkSws7ONB0d8e8SKJEy9uNNNM23inrDwRygl8omw5eLjbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9728894a88560be80f36e0bee78a2a71b274dce595a03a03dce790586a083fdb","last_reissued_at":"2026-05-20T00:00:51.610145Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:00:51.610145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15301","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":"2026-05-20T00:00:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pGUNC9GBRNrD75zxTGTpUyLlGDG1OCN+3KBYYWgeEfa0ReO0Cguj9OGAUblV3WAfHjchTKacJL/OSg5v2Q2TDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T15:47:50.483708Z"},"content_sha256":"8d4bf670ef145141ee56e13bd83669ac3608fbfbb7732912e127651c558617a3","schema_version":"1.0","event_id":"sha256:8d4bf670ef145141ee56e13bd83669ac3608fbfbb7732912e127651c558617a3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:S4UISSUIKYF6QDZW4C7OPCRKOG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Solvita achieves state-of-the-art results in competitive programming by letting LLM agents continuously learn from past outcomes using updatable knowledge networks.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chenchen Liu, Chenyu Wang, Chong Zheng, Han Li, Jiaheng Liu, Jinyu Tian, Letian Zhu, Rili Feng, Shihao Li, Weihao Xie, Xinping Lei, Yifan Yao, Yuqiao Du","submitted_at":"2026-05-14T18:15:09Z","abstract_excerpt":"Large language models (LLMs) still struggle with the rigorous reasoning demands of hard competitive programming. While recent multi-agent frameworks attempt to bridge this reliability gap, they remain fundamentally stateless: they rely on static retrieval and discard the valuable problem-solving and debugging experience gained from previous tasks. To address this, we present Solvita, an agentic evolution framework that enables continuous learning without requiring weight updates to the underlying LLM. Solvita reorganizes problem-solving into a closed-loop system of strategy selection, program "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Solvita establishes a new state-of-the-art among code-generation agents, outperforming existing multi-agent pipelines and nearly doubling the accuracy of single-pass baselines.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That outcome signals such as pass/fail verdicts, test certification quality, and adversarial vulnerabilities can be recast as effective reinforcement learning updates to the graph-structured knowledge network weights to produce transferable reasoning experience.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Solvita is an agentic evolution system using Planner, Solver, Oracle, and Hacker agents with trainable graph knowledge networks updated by reinforcement learning on pass/fail and vulnerability signals to achieve SOTA code generation performance.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Solvita achieves state-of-the-art results in competitive programming by letting LLM agents continuously learn from past outcomes using updatable knowledge networks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"39005b8403363f20d41c0889fe16c99c7b8055fc84e5c5d9ca495b346dee5ef6"},"source":{"id":"2605.15301","kind":"arxiv","version":1},"verdict":{"id":"47b116ec-ccbc-4eaf-ab63-97d0a5ae6074","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T16:23:24.195639Z","strongest_claim":"Solvita establishes a new state-of-the-art among code-generation agents, outperforming existing multi-agent pipelines and nearly doubling the accuracy of single-pass baselines.","one_line_summary":"Solvita is an agentic evolution system using Planner, Solver, Oracle, and Hacker agents with trainable graph knowledge networks updated by reinforcement learning on pass/fail and vulnerability signals to achieve SOTA code generation performance.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That outcome signals such as pass/fail verdicts, test certification quality, and adversarial vulnerabilities can be recast as effective reinforcement learning updates to the graph-structured knowledge network weights to produce transferable reasoning experience.","pith_extraction_headline":"Solvita achieves state-of-the-art results in competitive programming by letting LLM agents continuously learn from past outcomes using updatable knowledge networks."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15301/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T16:35:47.943257Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T16:31:18.347544Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:41:54.228702Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.780008Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"5d0830480655e942c488c6f045ebadeabfa53247953d56e9d08cbff998958938"},"references":{"count":130,"sample":[{"doi":"","year":2022,"title":"Competition-level code generation with AlphaCode.Science, 378(6624):1092–1097","work_id":"08577452-c193-40fb-8006-58920cff4ca3","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Measuring coding challenge competence with APPS","work_id":"092dbd8d-cb5a-4ef5-8399-5a94694c2c3a","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Can language models solve olympiad programming?arXiv preprint arXiv:2404.10952, 2024","work_id":"6aa8515f-bf86-4cb8-b0e3-b500c396feff","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","work_id":"ea9e51ce-1e75-4182-92d8-4d25f70d2ee4","ref_index":4,"cited_arxiv_id":"2403.07974","is_internal_anchor":true},{"doi":"","year":2021,"title":"Evaluating Large Language Models Trained on Code","work_id":"042493e9-b26f-4b4e-bbde-382072ca9b08","ref_index":5,"cited_arxiv_id":"2107.03374","is_internal_anchor":true}],"resolved_work":130,"snapshot_sha256":"8b60bb97052ba2fac5a9cb7e513fb3af02508fd968f419872b9277edd33a0b15","internal_anchors":11},"formal_canon":{"evidence_count":2,"snapshot_sha256":"cf67ba19e06a5d1edc48a8ad0e8c5a7ec2919ba0372c6aaf6a0921cf4edc7ae1"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"47b116ec-ccbc-4eaf-ab63-97d0a5ae6074"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:00:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kVGWsTwfZVBa5jS6Pvu/SvYMtvLTLFPhWzDTKZfqytjdz7URiW+BN3NecHURfxGzat95ms4iNae2S8fsYoXsCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T15:47:50.484742Z"},"content_sha256":"ef8e1402c04f5b5b57c2384ba2f9a416e54f36d0c4fa176bf3ee27eafcaa719f","schema_version":"1.0","event_id":"sha256:ef8e1402c04f5b5b57c2384ba2f9a416e54f36d0c4fa176bf3ee27eafcaa719f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S4UISSUIKYF6QDZW4C7OPCRKOG/bundle.json","state_url":"https://pith.science/pith/S4UISSUIKYF6QDZW4C7OPCRKOG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S4UISSUIKYF6QDZW4C7OPCRKOG/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":"2026-05-22T15:47:50Z","links":{"resolver":"https://pith.science/pith/S4UISSUIKYF6QDZW4C7OPCRKOG","bundle":"https://pith.science/pith/S4UISSUIKYF6QDZW4C7OPCRKOG/bundle.json","state":"https://pith.science/pith/S4UISSUIKYF6QDZW4C7OPCRKOG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S4UISSUIKYF6QDZW4C7OPCRKOG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:S4UISSUIKYF6QDZW4C7OPCRKOG","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":"ef14b2524be4c23e7e7f0bfc293f5059018f0744526972431bcea2a3a3c7e19e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-14T18:15:09Z","title_canon_sha256":"d34b07cc836d56f8d823a4b728f7102f266f80326af7d78f3c2fd356132cf115"},"schema_version":"1.0","source":{"id":"2605.15301","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15301","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15301v1","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15301","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_12","alias_value":"S4UISSUIKYF6","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_16","alias_value":"S4UISSUIKYF6QDZW","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_8","alias_value":"S4UISSUI","created_at":"2026-05-20T00:00:51Z"}],"graph_snapshots":[{"event_id":"sha256:ef8e1402c04f5b5b57c2384ba2f9a416e54f36d0c4fa176bf3ee27eafcaa719f","target":"graph","created_at":"2026-05-20T00:00:51Z","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":"Solvita establishes a new state-of-the-art among code-generation agents, outperforming existing multi-agent pipelines and nearly doubling the accuracy of single-pass baselines."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That outcome signals such as pass/fail verdicts, test certification quality, and adversarial vulnerabilities can be recast as effective reinforcement learning updates to the graph-structured knowledge network weights to produce transferable reasoning experience."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Solvita is an agentic evolution system using Planner, Solver, Oracle, and Hacker agents with trainable graph knowledge networks updated by reinforcement learning on pass/fail and vulnerability signals to achieve SOTA code generation performance."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Solvita achieves state-of-the-art results in competitive programming by letting LLM agents continuously learn from past outcomes using updatable knowledge networks."}],"snapshot_sha256":"39005b8403363f20d41c0889fe16c99c7b8055fc84e5c5d9ca495b346dee5ef6"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"cf67ba19e06a5d1edc48a8ad0e8c5a7ec2919ba0372c6aaf6a0921cf4edc7ae1"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T16:35:47.943257Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T16:31:18.347544Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T14:41:54.228702Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.780008Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.15301/integrity.json","findings":[],"snapshot_sha256":"5d0830480655e942c488c6f045ebadeabfa53247953d56e9d08cbff998958938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) still struggle with the rigorous reasoning demands of hard competitive programming. While recent multi-agent frameworks attempt to bridge this reliability gap, they remain fundamentally stateless: they rely on static retrieval and discard the valuable problem-solving and debugging experience gained from previous tasks. To address this, we present Solvita, an agentic evolution framework that enables continuous learning without requiring weight updates to the underlying LLM. Solvita reorganizes problem-solving into a closed-loop system of strategy selection, program ","authors_text":"Chenchen Liu, Chenyu Wang, Chong Zheng, Han Li, Jiaheng Liu, Jinyu Tian, Letian Zhu, Rili Feng, Shihao Li, Weihao Xie, Xinping Lei, Yifan Yao, Yuqiao Du","cross_cats":[],"headline":"Solvita achieves state-of-the-art results in competitive programming by letting LLM agents continuously learn from past outcomes using updatable knowledge networks.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-14T18:15:09Z","title":"Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution"},"references":{"count":130,"internal_anchors":11,"resolved_work":130,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Competition-level code generation with AlphaCode.Science, 378(6624):1092–1097","work_id":"08577452-c193-40fb-8006-58920cff4ca3","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Measuring coding challenge competence with APPS","work_id":"092dbd8d-cb5a-4ef5-8399-5a94694c2c3a","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Can language models solve olympiad programming?arXiv preprint arXiv:2404.10952, 2024","work_id":"6aa8515f-bf86-4cb8-b0e3-b500c396feff","year":2024},{"cited_arxiv_id":"2403.07974","doi":"","is_internal_anchor":true,"ref_index":4,"title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","work_id":"ea9e51ce-1e75-4182-92d8-4d25f70d2ee4","year":2024},{"cited_arxiv_id":"2107.03374","doi":"","is_internal_anchor":true,"ref_index":5,"title":"Evaluating Large Language Models Trained on Code","work_id":"042493e9-b26f-4b4e-bbde-382072ca9b08","year":2021}],"snapshot_sha256":"8b60bb97052ba2fac5a9cb7e513fb3af02508fd968f419872b9277edd33a0b15"},"source":{"id":"2605.15301","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T16:23:24.195639Z","id":"47b116ec-ccbc-4eaf-ab63-97d0a5ae6074","model_set":{"reader":"grok-4.3"},"one_line_summary":"Solvita is an agentic evolution system using Planner, Solver, Oracle, and Hacker agents with trainable graph knowledge networks updated by reinforcement learning on pass/fail and vulnerability signals to achieve SOTA code generation performance.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Solvita achieves state-of-the-art results in competitive programming by letting LLM agents continuously learn from past outcomes using updatable knowledge networks.","strongest_claim":"Solvita establishes a new state-of-the-art among code-generation agents, outperforming existing multi-agent pipelines and nearly doubling the accuracy of single-pass baselines.","weakest_assumption":"That outcome signals such as pass/fail verdicts, test certification quality, and adversarial vulnerabilities can be recast as effective reinforcement learning updates to the graph-structured knowledge network weights to produce transferable reasoning experience."}},"verdict_id":"47b116ec-ccbc-4eaf-ab63-97d0a5ae6074"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8d4bf670ef145141ee56e13bd83669ac3608fbfbb7732912e127651c558617a3","target":"record","created_at":"2026-05-20T00:00:51Z","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":"ef14b2524be4c23e7e7f0bfc293f5059018f0744526972431bcea2a3a3c7e19e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-14T18:15:09Z","title_canon_sha256":"d34b07cc836d56f8d823a4b728f7102f266f80326af7d78f3c2fd356132cf115"},"schema_version":"1.0","source":{"id":"2605.15301","kind":"arxiv","version":1}},"canonical_sha256":"9728894a88560be80f36e0bee78a2a71b274dce595a03a03dce790586a083fdb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9728894a88560be80f36e0bee78a2a71b274dce595a03a03dce790586a083fdb","first_computed_at":"2026-05-20T00:00:51.610145Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:00:51.610145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XLnpnkTqDah9pGci8CUUOVGwOqzkphZPBIAXLp1mkSws7ONB0d8e8SKJEy9uNNNM23inrDwRygl8omw5eLjbDw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:00:51.610963Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15301","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8d4bf670ef145141ee56e13bd83669ac3608fbfbb7732912e127651c558617a3","sha256:ef8e1402c04f5b5b57c2384ba2f9a416e54f36d0c4fa176bf3ee27eafcaa719f"],"state_sha256":"29ede07ce4da3f80627cc72db54ed200e89e74a25a9f1c6fed5f9c29ad9f6322"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"02nSGH8hr0hhYHPU59cFKMBo+07J4QIHg645qVhzGG1V/YIMTHHOmVjD/K+Cxra5761j87VZnzP4lbm0ahUgDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T15:47:50.489467Z","bundle_sha256":"299d0890ca2baedee84e3c8ac1810e8d628a7107cfe7ac7574ace867e0b72b5e"}}