{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:DEJPA3Z57HRBELNYKPSFPHSMQD","short_pith_number":"pith:DEJPA3Z5","canonical_record":{"source":{"id":"2207.04672","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-07-11T07:33:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"01ed8c3f5073036312b0a2e1fc34f8dfd220e1d0182d756cd9061ffa8c81e502","abstract_canon_sha256":"f38df21833913da5e913b5c418119c400fbbc9f7ebbf02bbc3229f86d01400be"},"schema_version":"1.0"},"canonical_sha256":"1912f06f3df9e2122db853e4579e4c80e77f0c8c251724fb6bec603e3d111512","source":{"kind":"arxiv","id":"2207.04672","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.04672","created_at":"2026-07-05T04:51:35Z"},{"alias_kind":"arxiv_version","alias_value":"2207.04672v3","created_at":"2026-07-05T04:51:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.04672","created_at":"2026-07-05T04:51:35Z"},{"alias_kind":"pith_short_12","alias_value":"DEJPA3Z57HRB","created_at":"2026-07-05T04:51:35Z"},{"alias_kind":"pith_short_16","alias_value":"DEJPA3Z57HRBELNY","created_at":"2026-07-05T04:51:35Z"},{"alias_kind":"pith_short_8","alias_value":"DEJPA3Z5","created_at":"2026-07-05T04:51:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:DEJPA3Z57HRBELNYKPSFPHSMQD","target":"record","payload":{"canonical_record":{"source":{"id":"2207.04672","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-07-11T07:33:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"01ed8c3f5073036312b0a2e1fc34f8dfd220e1d0182d756cd9061ffa8c81e502","abstract_canon_sha256":"f38df21833913da5e913b5c418119c400fbbc9f7ebbf02bbc3229f86d01400be"},"schema_version":"1.0"},"canonical_sha256":"1912f06f3df9e2122db853e4579e4c80e77f0c8c251724fb6bec603e3d111512","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:51:35.136817Z","signature_b64":"0/gRapn12wYSkuGmBTJnx9UtG1Vx+9+1J9HMEk/qhITpHJiUqCMa87ahy/9SKMDrZDtaiuclxSTgYJCrqt3gBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1912f06f3df9e2122db853e4579e4c80e77f0c8c251724fb6bec603e3d111512","last_reissued_at":"2026-07-05T04:51:35.136376Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:51:35.136376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.04672","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:51:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w79d6CVChTVQOfGBtzl0Lxh8Amk7JIQq+PCqU9534qunGoWxDnExGdPVfdDlgeGoAsbcmOMoXTI1XdkORtWMCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T16:18:48.169794Z"},"content_sha256":"c0cbc780103fa325e62a6d3b6d273716d1678d85b260723d1c23a9034be9320d","schema_version":"1.0","event_id":"sha256:c0cbc780103fa325e62a6d3b6d273716d1678d85b260723d1c23a9034be9320d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:DEJPA3Z57HRBELNYKPSFPHSMQD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"No Language Left Behind: Scaling Human-Centered Machine Translation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"A sparsely gated mixture of experts model trained on mined low-resource data achieves 44% relative BLEU improvement in translating 200 languages.","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alexandre Mourachko, Al Youngblood, Angela Fan, Anna Sun, Bapi Akula, Chau Tran, Christophe Ropers, Cynthia Gao, Daniel Licht, Dirk Rowe, Elahe Kalbassi, Francisco Guzm\\'an, Gabriel Mejia Gonzalez, Guillaume Wenzek, Holger Schwenk, James Cross, Janice Lam, Jean Maillard, Jeff Wang (NLLB Team), John Hoffman, Kaushik Ram Sadagopan, Kenneth Heafield, Kevin Heffernan, Loic Barrault, Maha Elbayad, Marta R. Costa-juss\\`a, Necip Fazil Ayan, NLLB Team, Onur \\c{C}elebi, Philipp Koehn, Pierre Andrews, Prangthip Hansanti, Safiyyah Saleem, Semarley Jarrett, Sergey Edunov, Shannon Spruit, Shruti Bhosale, Skyler Wang, Vedanuj Goswami","submitted_at":"2022-07-11T07:33:36Z","abstract_excerpt":"Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by first contextualizing the need for low-resource language translation support through ex"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The novel data mining techniques and architectural/training improvements produce genuinely higher-quality and safer translations for low-resource languages rather than merely fitting the new benchmark or human raters.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A sparsely gated mixture-of-experts model trained on newly mined low-resource data achieves 44% relative BLEU improvement across 200 languages while adding human safety evaluation.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A sparsely gated mixture of experts model trained on mined low-resource data achieves 44% relative BLEU improvement in translating 200 languages.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"44ba93e32fb0cbbebd1ae1d0318e1be53e526ca60ee6687cd65907d47672a8e9"},"source":{"id":"2207.04672","kind":"arxiv","version":3},"verdict":{"id":"9bda6551-a1a6-41fa-92bf-b973a7f99f4e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-12T17:48:09.914595Z","strongest_claim":"Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.","one_line_summary":"A sparsely gated mixture-of-experts model trained on newly mined low-resource data achieves 44% relative BLEU improvement across 200 languages while adding human safety evaluation.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The novel data mining techniques and architectural/training improvements produce genuinely higher-quality and safer translations for low-resource languages rather than merely fitting the new benchmark or human raters.","pith_extraction_headline":"A sparsely gated mixture of experts model trained on mined low-resource data achieves 44% relative BLEU improvement in translating 200 languages."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2207.04672/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":16,"sample":[{"doi":"","year":null,"title":"URL https://arxiv.org/abs/2110.03036. Benjamin Akera, Jonathan Mukiibi, Lydia Sanyu Naggayi, Claire Babirye, Isaac Owomugisha, Solomon Nsumba, Joyce Nakatumba-Nabende, Engineer Bainomugisha, Ernest Mw","work_id":"414b85bf-6e31-46c3-acb3-2b154373a894","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ondřej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R","work_id":"e45c1c77-a3b8-4489-996b-3a894895dde2","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.18653/v1/","year":2016,"title":"In: Zong, C., Xia, F., Li, W., Navigli, R","work_id":"8d675bdd-79ca-48d6-9163-fc17ce0e8ece","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.18653/v1/2021.iwslt-1.1","year":2021,"title":"doi: 10.18653/v1/2021.iwslt-1.1","work_id":"9da1e387-a255-4516-93af-7bb3533e17d3","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.18653/v1/2020.acl-main.485","year":2022,"title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","work_id":"1fe8c7c8-aff7-4b94-9096-e549d7e60789","ref_index":5,"cited_arxiv_id":"1308.3432","is_internal_anchor":true}],"resolved_work":16,"snapshot_sha256":"6b48e14dea6f5c9e7f65d1d65fb16e21b55a0768b0977e6deb0327b11c4c8bd2","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"072a74064a96f3c39e78b2e023745ea6bda1bd9326bb5ecac5425ff7571026d4"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"9bda6551-a1a6-41fa-92bf-b973a7f99f4e"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:51:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qn8svg/3ahsXosJsk1qdoIHwNw434ceaOFHBjwpaVQ3Xr7PrstOSmSUfqmqnRSSOnuepCv1Q6YEv7lNM6XnbBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T16:18:48.170833Z"},"content_sha256":"532b5e9ba02fea131025baa62109583ffe4485a2570027cfd570077a316ecadd","schema_version":"1.0","event_id":"sha256:532b5e9ba02fea131025baa62109583ffe4485a2570027cfd570077a316ecadd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DEJPA3Z57HRBELNYKPSFPHSMQD/bundle.json","state_url":"https://pith.science/pith/DEJPA3Z57HRBELNYKPSFPHSMQD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DEJPA3Z57HRBELNYKPSFPHSMQD/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":"2026-07-31T16:18:48Z","links":{"resolver":"https://pith.science/pith/DEJPA3Z57HRBELNYKPSFPHSMQD","bundle":"https://pith.science/pith/DEJPA3Z57HRBELNYKPSFPHSMQD/bundle.json","state":"https://pith.science/pith/DEJPA3Z57HRBELNYKPSFPHSMQD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DEJPA3Z57HRBELNYKPSFPHSMQD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:DEJPA3Z57HRBELNYKPSFPHSMQD","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":"f38df21833913da5e913b5c418119c400fbbc9f7ebbf02bbc3229f86d01400be","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-07-11T07:33:36Z","title_canon_sha256":"01ed8c3f5073036312b0a2e1fc34f8dfd220e1d0182d756cd9061ffa8c81e502"},"schema_version":"1.0","source":{"id":"2207.04672","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.04672","created_at":"2026-07-05T04:51:35Z"},{"alias_kind":"arxiv_version","alias_value":"2207.04672v3","created_at":"2026-07-05T04:51:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.04672","created_at":"2026-07-05T04:51:35Z"},{"alias_kind":"pith_short_12","alias_value":"DEJPA3Z57HRB","created_at":"2026-07-05T04:51:35Z"},{"alias_kind":"pith_short_16","alias_value":"DEJPA3Z57HRBELNY","created_at":"2026-07-05T04:51:35Z"},{"alias_kind":"pith_short_8","alias_value":"DEJPA3Z5","created_at":"2026-07-05T04:51:35Z"}],"graph_snapshots":[{"event_id":"sha256:532b5e9ba02fea131025baa62109583ffe4485a2570027cfd570077a316ecadd","target":"graph","created_at":"2026-07-05T04:51:35Z","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 improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The novel data mining techniques and architectural/training improvements produce genuinely higher-quality and safer translations for low-resource languages rather than merely fitting the new benchmark or human raters."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A sparsely gated mixture-of-experts model trained on newly mined low-resource data achieves 44% relative BLEU improvement across 200 languages while adding human safety evaluation."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A sparsely gated mixture of experts model trained on mined low-resource data achieves 44% relative BLEU improvement in translating 200 languages."}],"snapshot_sha256":"44ba93e32fb0cbbebd1ae1d0318e1be53e526ca60ee6687cd65907d47672a8e9"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"072a74064a96f3c39e78b2e023745ea6bda1bd9326bb5ecac5425ff7571026d4"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2207.04672/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by first contextualizing the need for low-resource language translation support through ex","authors_text":"Alexandre Mourachko, Al Youngblood, Angela Fan, Anna Sun, Bapi Akula, Chau Tran, Christophe Ropers, Cynthia Gao, Daniel Licht, Dirk Rowe, Elahe Kalbassi, Francisco Guzm\\'an, Gabriel Mejia Gonzalez, Guillaume Wenzek, Holger Schwenk, James Cross, Janice Lam, Jean Maillard, Jeff Wang (NLLB Team), John Hoffman, Kaushik Ram Sadagopan, Kenneth Heafield, Kevin Heffernan, Loic Barrault, Maha Elbayad, Marta R. Costa-juss\\`a, Necip Fazil Ayan, NLLB Team, Onur \\c{C}elebi, Philipp Koehn, Pierre Andrews, Prangthip Hansanti, Safiyyah Saleem, Semarley Jarrett, Sergey Edunov, Shannon Spruit, Shruti Bhosale, Skyler Wang, Vedanuj Goswami","cross_cats":["cs.AI"],"headline":"A sparsely gated mixture of experts model trained on mined low-resource data achieves 44% relative BLEU improvement in translating 200 languages.","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-07-11T07:33:36Z","title":"No Language Left Behind: Scaling Human-Centered Machine Translation"},"references":{"count":16,"internal_anchors":2,"resolved_work":16,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"URL https://arxiv.org/abs/2110.03036. Benjamin Akera, Jonathan Mukiibi, Lydia Sanyu Naggayi, Claire Babirye, Isaac Owomugisha, Solomon Nsumba, Joyce Nakatumba-Nabende, Engineer Bainomugisha, Ernest Mw","work_id":"414b85bf-6e31-46c3-acb3-2b154373a894","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ondřej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R","work_id":"e45c1c77-a3b8-4489-996b-3a894895dde2","year":2021},{"cited_arxiv_id":"","doi":"10.18653/v1/","is_internal_anchor":false,"ref_index":3,"title":"In: Zong, C., Xia, F., Li, W., Navigli, R","work_id":"8d675bdd-79ca-48d6-9163-fc17ce0e8ece","year":2016},{"cited_arxiv_id":"","doi":"10.18653/v1/2021.iwslt-1.1","is_internal_anchor":false,"ref_index":4,"title":"doi: 10.18653/v1/2021.iwslt-1.1","work_id":"9da1e387-a255-4516-93af-7bb3533e17d3","year":2021},{"cited_arxiv_id":"1308.3432","doi":"10.18653/v1/2020.acl-main.485","is_internal_anchor":true,"ref_index":5,"title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","work_id":"1fe8c7c8-aff7-4b94-9096-e549d7e60789","year":2022}],"snapshot_sha256":"6b48e14dea6f5c9e7f65d1d65fb16e21b55a0768b0977e6deb0327b11c4c8bd2"},"source":{"id":"2207.04672","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-12T17:48:09.914595Z","id":"9bda6551-a1a6-41fa-92bf-b973a7f99f4e","model_set":{"reader":"grok-4.3"},"one_line_summary":"A sparsely gated mixture-of-experts model trained on newly mined low-resource data achieves 44% relative BLEU improvement across 200 languages while adding human safety evaluation.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A sparsely gated mixture of experts model trained on mined low-resource data achieves 44% relative BLEU improvement in translating 200 languages.","strongest_claim":"Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.","weakest_assumption":"The novel data mining techniques and architectural/training improvements produce genuinely higher-quality and safer translations for low-resource languages rather than merely fitting the new benchmark or human raters."}},"verdict_id":"9bda6551-a1a6-41fa-92bf-b973a7f99f4e"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c0cbc780103fa325e62a6d3b6d273716d1678d85b260723d1c23a9034be9320d","target":"record","created_at":"2026-07-05T04:51:35Z","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":"f38df21833913da5e913b5c418119c400fbbc9f7ebbf02bbc3229f86d01400be","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-07-11T07:33:36Z","title_canon_sha256":"01ed8c3f5073036312b0a2e1fc34f8dfd220e1d0182d756cd9061ffa8c81e502"},"schema_version":"1.0","source":{"id":"2207.04672","kind":"arxiv","version":3}},"canonical_sha256":"1912f06f3df9e2122db853e4579e4c80e77f0c8c251724fb6bec603e3d111512","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1912f06f3df9e2122db853e4579e4c80e77f0c8c251724fb6bec603e3d111512","first_computed_at":"2026-07-05T04:51:35.136376Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:51:35.136376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0/gRapn12wYSkuGmBTJnx9UtG1Vx+9+1J9HMEk/qhITpHJiUqCMa87ahy/9SKMDrZDtaiuclxSTgYJCrqt3gBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:51:35.136817Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.04672","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c0cbc780103fa325e62a6d3b6d273716d1678d85b260723d1c23a9034be9320d","sha256:532b5e9ba02fea131025baa62109583ffe4485a2570027cfd570077a316ecadd"],"state_sha256":"a6f8a44522ab1359f44192171ee222cabd494ed1df21734f7b90131f0d20f6db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SO6ql6qxCvpA8IwKoOdNMagZDOjUGqr1YEfIrpa3TPRo+LNcZ++S4mh+FYEQwnz3yUW1XHlJS7cnnn4CuafjCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T16:18:48.181175Z","bundle_sha256":"d02a5ef685c8653c79ef6d0235645c0dc01a8c7e7d2c302429ef5141e578f406"}}