{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:TGO7KOIOENHQWSHUWRC5VAVLHF","short_pith_number":"pith:TGO7KOIO","canonical_record":{"source":{"id":"1705.03551","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-09T21:35:07Z","cross_cats_sorted":[],"title_canon_sha256":"ef3ca543d731c48932a97285444b002617d32324398f6fea5497a4092249ad0e","abstract_canon_sha256":"7531548ea4f5e06dfa132a2469bef33feff3da1016bc365957eacddc5b925fbf"},"schema_version":"1.0"},"canonical_sha256":"999df5390e234f0b48f4b445da82ab397111634f2097cc85ab5cd043f3ec1b61","source":{"kind":"arxiv","id":"1705.03551","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.03551","created_at":"2026-07-04T21:56:46Z"},{"alias_kind":"arxiv_version","alias_value":"1705.03551v2","created_at":"2026-07-04T21:56:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.03551","created_at":"2026-07-04T21:56:46Z"},{"alias_kind":"pith_short_12","alias_value":"TGO7KOIOENHQ","created_at":"2026-07-04T21:56:46Z"},{"alias_kind":"pith_short_16","alias_value":"TGO7KOIOENHQWSHU","created_at":"2026-07-04T21:56:46Z"},{"alias_kind":"pith_short_8","alias_value":"TGO7KOIO","created_at":"2026-07-04T21:56:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:TGO7KOIOENHQWSHUWRC5VAVLHF","target":"record","payload":{"canonical_record":{"source":{"id":"1705.03551","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-09T21:35:07Z","cross_cats_sorted":[],"title_canon_sha256":"ef3ca543d731c48932a97285444b002617d32324398f6fea5497a4092249ad0e","abstract_canon_sha256":"7531548ea4f5e06dfa132a2469bef33feff3da1016bc365957eacddc5b925fbf"},"schema_version":"1.0"},"canonical_sha256":"999df5390e234f0b48f4b445da82ab397111634f2097cc85ab5cd043f3ec1b61","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T21:56:46.651110Z","signature_b64":"iCSCfjiuXRUWF4FqFwbDx5GwoeJAQIj/djWDtaT0SqaWnvzBGVznoTVDcxDUdpbcUv7OzCAULmCChnL+QOh4Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"999df5390e234f0b48f4b445da82ab397111634f2097cc85ab5cd043f3ec1b61","last_reissued_at":"2026-07-04T21:56:46.650576Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T21:56:46.650576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.03551","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T21:56:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+ca1mgr3oU62fQ5UOlulC6l3CZgEiWbnqOkY7tFhCYrflGmVO3+f/4yRkhcYcKF3q1xaPLqY5yj4r2oOQei4Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:13:18.406996Z"},"content_sha256":"cdcd36df64a9b0a731953664f2110a1c145216d4aca2087299c69dffb25f6921","schema_version":"1.0","event_id":"sha256:cdcd36df64a9b0a731953664f2110a1c145216d4aca2087299c69dffb25f6921"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:TGO7KOIOENHQWSHUWRC5VAVLHF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"TriviaQA introduces a distant-supervision dataset of 95,000 trivia questions and evidence documents where current models reach only 40 percent accuracy against 80 percent for humans.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daniel S. Weld, Eunsol Choi, Luke Zettlemoyer, Mandar Joshi","submitted_at":"2017-05-09T21:35:07Z","abstract_excerpt":"We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence senten"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The independently gathered evidence documents provide high quality distant supervision sufficient for answering the questions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"TriviaQA is a new large-scale dataset for reading comprehension that features complex compositional questions, high lexical variability, and cross-sentence reasoning requirements, where current baselines reach only 40% while humans reach 80%.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"TriviaQA introduces a distant-supervision dataset of 95,000 trivia questions and evidence documents where current models reach only 40 percent accuracy against 80 percent for humans.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"a362bd9a177f99fcc1c3bf451fdd2262b62b213db36208b2738587b486e0664e"},"source":{"id":"1705.03551","kind":"arxiv","version":2},"verdict":{"id":"d963bb0c-ff1f-4a44-9e2c-bfd805108b42","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T14:54:48.532364Z","strongest_claim":"Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study.","one_line_summary":"TriviaQA is a new large-scale dataset for reading comprehension that features complex compositional questions, high lexical variability, and cross-sentence reasoning requirements, where current baselines reach only 40% while humans reach 80%.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The independently gathered evidence documents provide high quality distant supervision sufficient for answering the questions.","pith_extraction_headline":"TriviaQA introduces a distant-supervision dataset of 95,000 trivia questions and evidence documents where current models reach only 40 percent accuracy against 80 percent for humans."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1705.03551/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":33,"sample":[{"doi":"","year":2013,"title":"Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. Semantic parsing on freebase from question-answer pairs http://aclweb.org/anthology/D/D13/D13-1160.pdf. In Proceedings of the 2013 Con","work_id":"561797dd-61a9-44df-970a-94e427539cdd","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2015,"title":"Large-scale Simple Question Answering with Memory Networks","work_id":"f99ac6ec-9a3c-47f6-8a5d-d44a87cc5310","ref_index":2,"cited_arxiv_id":"1506.02075","is_internal_anchor":false},{"doi":"","year":2012,"title":"Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daum\\' e III. 2012. Besting the quiz master: Crowdsourcing incremental classification games http://www.aclweb.org/anthology/D12-1118. In Proceeding","work_id":"6c7a7989-d2ab-433b-87d3-5ff2bc71f113","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2013,"title":"Qingqing Cai and Alexander Yates. 2013. Large-scale semantic parsing via schema matching and lexicon extension http://www.aclweb.org/anthology/P13-1042. In Proceedings of the 51st Annual Meeting of th","work_id":"aebbd049-394e-44c3-8181-895ed8de462c","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"Danqi Chen, Jason Bolton, and Christopher D. Manning. 2016. A thorough examination of the cnn/daily mail reading comprehension task http://www.aclweb.org/anthology/P16-1223. In Proceedings of the 54th","work_id":"1e8429dc-e75e-4896-8330-97208ffe7c07","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":33,"snapshot_sha256":"370c41f5cac702bb17a3af2b0bee86f6bae37828b1f1ea4ff084d090b5b713af","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":"d963bb0c-ff1f-4a44-9e2c-bfd805108b42"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T21:56:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UDP2rIe83l30R2PxwNS+KSBL7yrVs0eD8oP8e2tPyVq2rxvtJlLE51XlF4l3t1uM/4l+ndJt6PyYMGxUV/c4DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:13:18.407955Z"},"content_sha256":"1de56df14b63d556c9dc6fd162217708d3615eb3f44307e4f4931c5c3b35002e","schema_version":"1.0","event_id":"sha256:1de56df14b63d556c9dc6fd162217708d3615eb3f44307e4f4931c5c3b35002e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TGO7KOIOENHQWSHUWRC5VAVLHF/bundle.json","state_url":"https://pith.science/pith/TGO7KOIOENHQWSHUWRC5VAVLHF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TGO7KOIOENHQWSHUWRC5VAVLHF/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":"2026-07-07T07:13:18Z","links":{"resolver":"https://pith.science/pith/TGO7KOIOENHQWSHUWRC5VAVLHF","bundle":"https://pith.science/pith/TGO7KOIOENHQWSHUWRC5VAVLHF/bundle.json","state":"https://pith.science/pith/TGO7KOIOENHQWSHUWRC5VAVLHF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TGO7KOIOENHQWSHUWRC5VAVLHF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:TGO7KOIOENHQWSHUWRC5VAVLHF","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":"7531548ea4f5e06dfa132a2469bef33feff3da1016bc365957eacddc5b925fbf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-09T21:35:07Z","title_canon_sha256":"ef3ca543d731c48932a97285444b002617d32324398f6fea5497a4092249ad0e"},"schema_version":"1.0","source":{"id":"1705.03551","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.03551","created_at":"2026-07-04T21:56:46Z"},{"alias_kind":"arxiv_version","alias_value":"1705.03551v2","created_at":"2026-07-04T21:56:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.03551","created_at":"2026-07-04T21:56:46Z"},{"alias_kind":"pith_short_12","alias_value":"TGO7KOIOENHQ","created_at":"2026-07-04T21:56:46Z"},{"alias_kind":"pith_short_16","alias_value":"TGO7KOIOENHQWSHU","created_at":"2026-07-04T21:56:46Z"},{"alias_kind":"pith_short_8","alias_value":"TGO7KOIO","created_at":"2026-07-04T21:56:46Z"}],"graph_snapshots":[{"event_id":"sha256:1de56df14b63d556c9dc6fd162217708d3615eb3f44307e4f4931c5c3b35002e","target":"graph","created_at":"2026-07-04T21:56:46Z","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":"Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The independently gathered evidence documents provide high quality distant supervision sufficient for answering the questions."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"TriviaQA is a new large-scale dataset for reading comprehension that features complex compositional questions, high lexical variability, and cross-sentence reasoning requirements, where current baselines reach only 40% while humans reach 80%."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"TriviaQA introduces a distant-supervision dataset of 95,000 trivia questions and evidence documents where current models reach only 40 percent accuracy against 80 percent for humans."}],"snapshot_sha256":"a362bd9a177f99fcc1c3bf451fdd2262b62b213db36208b2738587b486e0664e"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1705.03551/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence senten","authors_text":"Daniel S. Weld, Eunsol Choi, Luke Zettlemoyer, Mandar Joshi","cross_cats":[],"headline":"TriviaQA introduces a distant-supervision dataset of 95,000 trivia questions and evidence documents where current models reach only 40 percent accuracy against 80 percent for humans.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-09T21:35:07Z","title":"TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension"},"references":{"count":33,"internal_anchors":2,"resolved_work":33,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. Semantic parsing on freebase from question-answer pairs http://aclweb.org/anthology/D/D13/D13-1160.pdf. In Proceedings of the 2013 Con","work_id":"561797dd-61a9-44df-970a-94e427539cdd","year":2013},{"cited_arxiv_id":"1506.02075","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Large-scale Simple Question Answering with Memory Networks","work_id":"f99ac6ec-9a3c-47f6-8a5d-d44a87cc5310","year":2015},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daum\\' e III. 2012. Besting the quiz master: Crowdsourcing incremental classification games http://www.aclweb.org/anthology/D12-1118. In Proceeding","work_id":"6c7a7989-d2ab-433b-87d3-5ff2bc71f113","year":2012},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Qingqing Cai and Alexander Yates. 2013. Large-scale semantic parsing via schema matching and lexicon extension http://www.aclweb.org/anthology/P13-1042. In Proceedings of the 51st Annual Meeting of th","work_id":"aebbd049-394e-44c3-8181-895ed8de462c","year":2013},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Danqi Chen, Jason Bolton, and Christopher D. Manning. 2016. A thorough examination of the cnn/daily mail reading comprehension task http://www.aclweb.org/anthology/P16-1223. In Proceedings of the 54th","work_id":"1e8429dc-e75e-4896-8330-97208ffe7c07","year":2016}],"snapshot_sha256":"370c41f5cac702bb17a3af2b0bee86f6bae37828b1f1ea4ff084d090b5b713af"},"source":{"id":"1705.03551","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-11T14:54:48.532364Z","id":"d963bb0c-ff1f-4a44-9e2c-bfd805108b42","model_set":{"reader":"grok-4.3"},"one_line_summary":"TriviaQA is a new large-scale dataset for reading comprehension that features complex compositional questions, high lexical variability, and cross-sentence reasoning requirements, where current baselines reach only 40% while humans reach 80%.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"TriviaQA introduces a distant-supervision dataset of 95,000 trivia questions and evidence documents where current models reach only 40 percent accuracy against 80 percent for humans.","strongest_claim":"Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study.","weakest_assumption":"The independently gathered evidence documents provide high quality distant supervision sufficient for answering the questions."}},"verdict_id":"d963bb0c-ff1f-4a44-9e2c-bfd805108b42"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:cdcd36df64a9b0a731953664f2110a1c145216d4aca2087299c69dffb25f6921","target":"record","created_at":"2026-07-04T21:56:46Z","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":"7531548ea4f5e06dfa132a2469bef33feff3da1016bc365957eacddc5b925fbf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-09T21:35:07Z","title_canon_sha256":"ef3ca543d731c48932a97285444b002617d32324398f6fea5497a4092249ad0e"},"schema_version":"1.0","source":{"id":"1705.03551","kind":"arxiv","version":2}},"canonical_sha256":"999df5390e234f0b48f4b445da82ab397111634f2097cc85ab5cd043f3ec1b61","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"999df5390e234f0b48f4b445da82ab397111634f2097cc85ab5cd043f3ec1b61","first_computed_at":"2026-07-04T21:56:46.650576Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T21:56:46.650576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iCSCfjiuXRUWF4FqFwbDx5GwoeJAQIj/djWDtaT0SqaWnvzBGVznoTVDcxDUdpbcUv7OzCAULmCChnL+QOh4Cg==","signature_status":"signed_v1","signed_at":"2026-07-04T21:56:46.651110Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.03551","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cdcd36df64a9b0a731953664f2110a1c145216d4aca2087299c69dffb25f6921","sha256:1de56df14b63d556c9dc6fd162217708d3615eb3f44307e4f4931c5c3b35002e"],"state_sha256":"37abac1a5b7048f1de2cc4fe31f67d999481d3d93c644dc5b94b25a5f6fef376"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1CGUGS9jglLSqVAbL2XY5fGtDBM5aR60MW2lweAmhz5rrrplyhRf+I0RG54FIxpxEoqysmd0DZ9E9mwSdTBPAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T07:13:18.413200Z","bundle_sha256":"feeda2c89ffc2b76da6a4a134ae0a7992cf082901d5b03c1a62448d38f59a7cf"}}