{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:E7CAGJ73MDIFJLVO7ZIEPVBGKO","short_pith_number":"pith:E7CAGJ73","canonical_record":{"source":{"id":"2107.03374","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-07T17:41:24Z","cross_cats_sorted":[],"title_canon_sha256":"f0f282ac098b861cfefe05a8fe0facf7bb9b787c0bc7d9be306ebfd7c070df9a","abstract_canon_sha256":"e55701aeeb9477c731545f15e8f4b0b7c46ab737b705a9e249f81f10b2da73bb"},"schema_version":"1.0"},"canonical_sha256":"27c40327fb60d054aeaefe5047d426539cdc4e04868cea5134b75a3d905bece9","source":{"kind":"arxiv","id":"2107.03374","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.03374","created_at":"2026-07-05T02:57:55Z"},{"alias_kind":"arxiv_version","alias_value":"2107.03374v2","created_at":"2026-07-05T02:57:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.03374","created_at":"2026-07-05T02:57:55Z"},{"alias_kind":"pith_short_12","alias_value":"E7CAGJ73MDIF","created_at":"2026-07-05T02:57:55Z"},{"alias_kind":"pith_short_16","alias_value":"E7CAGJ73MDIFJLVO","created_at":"2026-07-05T02:57:55Z"},{"alias_kind":"pith_short_8","alias_value":"E7CAGJ73","created_at":"2026-07-05T02:57:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:E7CAGJ73MDIFJLVO7ZIEPVBGKO","target":"record","payload":{"canonical_record":{"source":{"id":"2107.03374","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-07T17:41:24Z","cross_cats_sorted":[],"title_canon_sha256":"f0f282ac098b861cfefe05a8fe0facf7bb9b787c0bc7d9be306ebfd7c070df9a","abstract_canon_sha256":"e55701aeeb9477c731545f15e8f4b0b7c46ab737b705a9e249f81f10b2da73bb"},"schema_version":"1.0"},"canonical_sha256":"27c40327fb60d054aeaefe5047d426539cdc4e04868cea5134b75a3d905bece9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:57:55.574301Z","signature_b64":"/x+s9+xGYs4IZMLKWwHPw/pu0lxymkqBRJW8pa2Z58DWFnZSiioPxckbO2zGc4FgxFL5CkmRrtPi4zEZQVsaAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27c40327fb60d054aeaefe5047d426539cdc4e04868cea5134b75a3d905bece9","last_reissued_at":"2026-07-05T02:57:55.573867Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:57:55.573867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.03374","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":"2026-07-05T02:57:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vLDh14vhq5+Q+kS+GQ9XUi/EP+peBTo+AMQdYspzKX3bbq67slepseFCjp5spOd2haI3vHlQFKBI3XStGL2IBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T07:30:20.690342Z"},"content_sha256":"4a00bd85ff95dcb5403a53d081f54b31bdfdd27005d0519edf791bb4ca7e9793","schema_version":"1.0","event_id":"sha256:4a00bd85ff95dcb5403a53d081f54b31bdfdd27005d0519edf791bb4ca7e9793"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:E7CAGJ73MDIFJLVO7ZIEPVBGKO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evaluating Large Language Models Trained on Code","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A language model fine-tuned on GitHub code solves 28.8 percent of problems on a new benchmark for writing programs from docstrings.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alec Radford, Alethea Power, Alex Nichol, Alex Paino, Alex Ray, Andrew N. Carr, Ariel Herbert-Voss, Bob McGrew, Brooke Chan, Christopher Hesse, Clemens Winter, Dario Amodei, Dave Cummings, Elizabeth Barnes, Evan Morikawa, Felipe Petroski Such, Fotios Chantzis, Girish Sastry, Greg Brockman, Gretchen Krueger, Harri Edwards, Heewoo Jun, Heidy Khlaaf, Henrique Ponde de Oliveira Pinto, Igor Babuschkin, Ilya Sutskever, Jan Leike, Jared Kaplan, Jerry Tworek, Jie Tang, Josh Achiam, Katie Mayer, Lukasz Kaiser, Mark Chen, Matthew Knight, Matthias Plappert, Michael Petrov, Mikhail Pavlov, Miles Brundage, Mira Murati, Mohammad Bavarian, Nicholas Joseph, Nick Ryder, Nikolas Tezak, Pamela Mishkin, Peter Welinder, Philippe Tillet, Qiming Yuan, Raul Puri, Sam McCandlish, Scott Gray, Shantanu Jain, Suchir Balaji, Vedant Misra, William Hebgen Guss, William Saunders, Wojciech Zaremba, Yuri Burda","submitted_at":"2021-07-07T17:41:24Z","abstract_excerpt":"We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of ou"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The HumanEval test cases and problems are free of contamination from the model's training data and provide a reliable measure of functional correctness that generalizes beyond the benchmark.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Codex achieves 28.8% pass@1 on HumanEval, rising to 70.2% with 100 samples per problem via repeated sampling.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A language model fine-tuned on GitHub code solves 28.8 percent of problems on a new benchmark for writing programs from docstrings.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"817d1acdf1de8d09c9462e6d6beefa67167decbb968e6581ac4badac3c9a03b5"},"source":{"id":"2107.03374","kind":"arxiv","version":2},"verdict":{"id":"d86269e6-5f9f-482f-955c-535187184664","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-24T12:17:58.540522Z","strongest_claim":"On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%.","one_line_summary":"Codex achieves 28.8% pass@1 on HumanEval, rising to 70.2% with 100 samples per problem via repeated sampling.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The HumanEval test cases and problems are free of contamination from the model's training data and provide a reliable measure of functional correctness that generalizes beyond the benchmark.","pith_extraction_headline":"A language model fine-tuned on GitHub code solves 28.8 percent of problems on a new benchmark for writing programs from docstrings."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.03374/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":22,"sample":[{"doi":"","year":2019,"title":"URL http://proceedings.mlr.press/ v37/allamanis15.html","work_id":"453ebac7-2aaa-4384-b2aa-2f73ad059753","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Generating Long Sequences with Sparse Transformers","work_id":"c5b81688-45ee-4a9a-b095-e6290f45cb6c","ref_index":2,"cited_arxiv_id":"1904.10509","is_internal_anchor":true},{"doi":"","year":2014,"title":"URL https://www.alignmentforum.org/ posts/ZeE7EKHTFMBs8eMxn/clarifying-ai- alignment. Clarkson, M. R., Finkbeiner, B., Koleini, M., Micinski, K. K., Rabe, M. N., and S´anchez, C. Temporal logics for h","work_id":"c353746a-8440-4c53-a0a8-61a77454086c","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1145/362566.362568","year":2021,"title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","work_id":"ed240a10-5b19-406c-baa5-30803f465785","ref_index":4,"cited_arxiv_id":"1810.04805","is_internal_anchor":true},{"doi":"","year":null,"title":"1 <= len(arr) <= 100","work_id":"f74995cf-e5d5-4ac1-83de-b283eb776323","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":22,"snapshot_sha256":"ad1b0ecf2e27f367dcf091fdb5afde834d1187152c191af905f2ee287d9df00f","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"dd3214c4702ca156ce26fa99325713ef2e75eb165974a72473ccc199df9bd4af"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"d86269e6-5f9f-482f-955c-535187184664"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:57:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"co2ePTeGVvXKWKjEAwc/gAoU49FaXKCklhI5aYSjsVEwa+F9s2g6J+Xhs0KIn3Q/cBamulqU5BAWG5IUQgroAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T07:30:20.691201Z"},"content_sha256":"cca7d661e32476b26c5474ac0939cf291bd4f3afff94f6326e2f399e9ecd1a3d","schema_version":"1.0","event_id":"sha256:cca7d661e32476b26c5474ac0939cf291bd4f3afff94f6326e2f399e9ecd1a3d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E7CAGJ73MDIFJLVO7ZIEPVBGKO/bundle.json","state_url":"https://pith.science/pith/E7CAGJ73MDIFJLVO7ZIEPVBGKO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E7CAGJ73MDIFJLVO7ZIEPVBGKO/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":"2026-08-10T07:30:20Z","links":{"resolver":"https://pith.science/pith/E7CAGJ73MDIFJLVO7ZIEPVBGKO","bundle":"https://pith.science/pith/E7CAGJ73MDIFJLVO7ZIEPVBGKO/bundle.json","state":"https://pith.science/pith/E7CAGJ73MDIFJLVO7ZIEPVBGKO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E7CAGJ73MDIFJLVO7ZIEPVBGKO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:E7CAGJ73MDIFJLVO7ZIEPVBGKO","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":"e55701aeeb9477c731545f15e8f4b0b7c46ab737b705a9e249f81f10b2da73bb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-07T17:41:24Z","title_canon_sha256":"f0f282ac098b861cfefe05a8fe0facf7bb9b787c0bc7d9be306ebfd7c070df9a"},"schema_version":"1.0","source":{"id":"2107.03374","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.03374","created_at":"2026-07-05T02:57:55Z"},{"alias_kind":"arxiv_version","alias_value":"2107.03374v2","created_at":"2026-07-05T02:57:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.03374","created_at":"2026-07-05T02:57:55Z"},{"alias_kind":"pith_short_12","alias_value":"E7CAGJ73MDIF","created_at":"2026-07-05T02:57:55Z"},{"alias_kind":"pith_short_16","alias_value":"E7CAGJ73MDIFJLVO","created_at":"2026-07-05T02:57:55Z"},{"alias_kind":"pith_short_8","alias_value":"E7CAGJ73","created_at":"2026-07-05T02:57:55Z"}],"graph_snapshots":[{"event_id":"sha256:cca7d661e32476b26c5474ac0939cf291bd4f3afff94f6326e2f399e9ecd1a3d","target":"graph","created_at":"2026-07-05T02:57:55Z","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 HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The HumanEval test cases and problems are free of contamination from the model's training data and provide a reliable measure of functional correctness that generalizes beyond the benchmark."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Codex achieves 28.8% pass@1 on HumanEval, rising to 70.2% with 100 samples per problem via repeated sampling."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A language model fine-tuned on GitHub code solves 28.8 percent of problems on a new benchmark for writing programs from docstrings."}],"snapshot_sha256":"817d1acdf1de8d09c9462e6d6beefa67167decbb968e6581ac4badac3c9a03b5"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"dd3214c4702ca156ce26fa99325713ef2e75eb165974a72473ccc199df9bd4af"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2107.03374/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of ou","authors_text":"Alec Radford, Alethea Power, Alex Nichol, Alex Paino, Alex Ray, Andrew N. Carr, Ariel Herbert-Voss, Bob McGrew, Brooke Chan, Christopher Hesse, Clemens Winter, Dario Amodei, Dave Cummings, Elizabeth Barnes, Evan Morikawa, Felipe Petroski Such, Fotios Chantzis, Girish Sastry, Greg Brockman, Gretchen Krueger, Harri Edwards, Heewoo Jun, Heidy Khlaaf, Henrique Ponde de Oliveira Pinto, Igor Babuschkin, Ilya Sutskever, Jan Leike, Jared Kaplan, Jerry Tworek, Jie Tang, Josh Achiam, Katie Mayer, Lukasz Kaiser, Mark Chen, Matthew Knight, Matthias Plappert, Michael Petrov, Mikhail Pavlov, Miles Brundage, Mira Murati, Mohammad Bavarian, Nicholas Joseph, Nick Ryder, Nikolas Tezak, Pamela Mishkin, Peter Welinder, Philippe Tillet, Qiming Yuan, Raul Puri, Sam McCandlish, Scott Gray, Shantanu Jain, Suchir Balaji, Vedant Misra, William Hebgen Guss, William Saunders, Wojciech Zaremba, Yuri Burda","cross_cats":[],"headline":"A language model fine-tuned on GitHub code solves 28.8 percent of problems on a new benchmark for writing programs from docstrings.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code"},"references":{"count":22,"internal_anchors":2,"resolved_work":22,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"URL http://proceedings.mlr.press/ v37/allamanis15.html","work_id":"453ebac7-2aaa-4384-b2aa-2f73ad059753","year":2019},{"cited_arxiv_id":"1904.10509","doi":"","is_internal_anchor":true,"ref_index":2,"title":"Generating Long Sequences with Sparse Transformers","work_id":"c5b81688-45ee-4a9a-b095-e6290f45cb6c","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"URL https://www.alignmentforum.org/ posts/ZeE7EKHTFMBs8eMxn/clarifying-ai- alignment. Clarkson, M. R., Finkbeiner, B., Koleini, M., Micinski, K. K., Rabe, M. N., and S´anchez, C. Temporal logics for h","work_id":"c353746a-8440-4c53-a0a8-61a77454086c","year":2014},{"cited_arxiv_id":"1810.04805","doi":"10.1145/362566.362568","is_internal_anchor":true,"ref_index":4,"title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","work_id":"ed240a10-5b19-406c-baa5-30803f465785","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"1 <= len(arr) <= 100","work_id":"f74995cf-e5d5-4ac1-83de-b283eb776323","year":null}],"snapshot_sha256":"ad1b0ecf2e27f367dcf091fdb5afde834d1187152c191af905f2ee287d9df00f"},"source":{"id":"2107.03374","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-24T12:17:58.540522Z","id":"d86269e6-5f9f-482f-955c-535187184664","model_set":{"reader":"grok-4.3"},"one_line_summary":"Codex achieves 28.8% pass@1 on HumanEval, rising to 70.2% with 100 samples per problem via repeated sampling.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A language model fine-tuned on GitHub code solves 28.8 percent of problems on a new benchmark for writing programs from docstrings.","strongest_claim":"On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%.","weakest_assumption":"The HumanEval test cases and problems are free of contamination from the model's training data and provide a reliable measure of functional correctness that generalizes beyond the benchmark."}},"verdict_id":"d86269e6-5f9f-482f-955c-535187184664"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4a00bd85ff95dcb5403a53d081f54b31bdfdd27005d0519edf791bb4ca7e9793","target":"record","created_at":"2026-07-05T02:57:55Z","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":"e55701aeeb9477c731545f15e8f4b0b7c46ab737b705a9e249f81f10b2da73bb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-07T17:41:24Z","title_canon_sha256":"f0f282ac098b861cfefe05a8fe0facf7bb9b787c0bc7d9be306ebfd7c070df9a"},"schema_version":"1.0","source":{"id":"2107.03374","kind":"arxiv","version":2}},"canonical_sha256":"27c40327fb60d054aeaefe5047d426539cdc4e04868cea5134b75a3d905bece9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"27c40327fb60d054aeaefe5047d426539cdc4e04868cea5134b75a3d905bece9","first_computed_at":"2026-07-05T02:57:55.573867Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:57:55.573867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/x+s9+xGYs4IZMLKWwHPw/pu0lxymkqBRJW8pa2Z58DWFnZSiioPxckbO2zGc4FgxFL5CkmRrtPi4zEZQVsaAw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:57:55.574301Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.03374","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4a00bd85ff95dcb5403a53d081f54b31bdfdd27005d0519edf791bb4ca7e9793","sha256:cca7d661e32476b26c5474ac0939cf291bd4f3afff94f6326e2f399e9ecd1a3d"],"state_sha256":"48722c5ad39aefcc711eae4f6d1abe2a4ff311c5fdf8527e0a0196f9fef53db7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2PrTBVFeboRloOeLh+68SeVYR68bbUxRgtYW4bIhc7h+B9oivfSGcMAEM5oZxWcKxfgfCyMd9KW+Qi7zkZDuAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T07:30:20.709672Z","bundle_sha256":"87dc612f83d91d584bce03d715467b11eaf1aa6f6a9635ac20f9c9d38de1403f"}}