{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:4HCXU2UBLE6ERE6JHYJQ4DYVAG","short_pith_number":"pith:4HCXU2UB","canonical_record":{"source":{"id":"2308.12950","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-24T17:39:13Z","cross_cats_sorted":[],"title_canon_sha256":"f24ce93deac952ca680c24617d26a3d0bde497229d6443aa52f4331fe2f66a2a","abstract_canon_sha256":"59de633ed50b5b1789c46fe99f056c009fccebfd1681b21fd272fa1b8c263cf5"},"schema_version":"1.0"},"canonical_sha256":"e1c57a6a81593c4893c93e130e0f15019701171acf55b42e94537386d9f2d0d5","source":{"kind":"arxiv","id":"2308.12950","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.12950","created_at":"2026-07-05T07:40:01Z"},{"alias_kind":"arxiv_version","alias_value":"2308.12950v3","created_at":"2026-07-05T07:40:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.12950","created_at":"2026-07-05T07:40:01Z"},{"alias_kind":"pith_short_12","alias_value":"4HCXU2UBLE6E","created_at":"2026-07-05T07:40:01Z"},{"alias_kind":"pith_short_16","alias_value":"4HCXU2UBLE6ERE6J","created_at":"2026-07-05T07:40:01Z"},{"alias_kind":"pith_short_8","alias_value":"4HCXU2UB","created_at":"2026-07-05T07:40:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:4HCXU2UBLE6ERE6JHYJQ4DYVAG","target":"record","payload":{"canonical_record":{"source":{"id":"2308.12950","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-24T17:39:13Z","cross_cats_sorted":[],"title_canon_sha256":"f24ce93deac952ca680c24617d26a3d0bde497229d6443aa52f4331fe2f66a2a","abstract_canon_sha256":"59de633ed50b5b1789c46fe99f056c009fccebfd1681b21fd272fa1b8c263cf5"},"schema_version":"1.0"},"canonical_sha256":"e1c57a6a81593c4893c93e130e0f15019701171acf55b42e94537386d9f2d0d5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:40:01.031752Z","signature_b64":"r2P/5R74aiY0/yT1L5BYgLTdjzg/TwK5Ou817D6XV6TcEM+2Jp2IZkLJWO1GQHViNHUT3/ffPEuywnYMZ0psDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1c57a6a81593c4893c93e130e0f15019701171acf55b42e94537386d9f2d0d5","last_reissued_at":"2026-07-05T07:40:01.031250Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:40:01.031250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.12950","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":"2026-07-05T07:40:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3iJT4sLlSBsdPiv2vvOb+AmxKD0qnAqZshDwEekWBASrxZD4hQ+wWLC3NAYVzmazKgChVFnDll3ZUASYdl+rDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T19:05:27.024631Z"},"content_sha256":"5fea39dd111d3fb6c7edd0706962f2b61a0f695ecb5c5976c34a790b00bed119","schema_version":"1.0","event_id":"sha256:5fea39dd111d3fb6c7edd0706962f2b61a0f695ecb5c5976c34a790b00bed119"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:4HCXU2UBLE6ERE6JHYJQ4DYVAG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Code Llama: Open Foundation Models for Code","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Code Llama models achieve state-of-the-art results among open models on code benchmarks while adding infilling and long-context support.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aaron Grattafiori, Alexandre D\\'efossez, Artyom Kozhevnikov, Baptiste Rozi\\`ere, Cristian Canton Ferrer, Fabian Gloeckle, Faisal Azhar, Gabriel Synnaeve, Hugo Touvron, Itai Gat, Ivan Evtimov, Jade Copet, J\\'er\\'emy Rapin, Jingyu Liu, Joanna Bitton, Jonas Gehring, Louis Martin, Manish Bhatt, Nicolas Usunier, Romain Sauvestre, Sten Sootla, Tal Remez, Thomas Scialom, Wenhan Xiong, Xiaoqing Ellen Tan, Yossi Adi","submitted_at":"2023-08-24T17:39:13Z","abstract_excerpt":"We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama - Python), and instruction-following models (Code Llama - Instruct) with 7B, 13B, 34B and 70B parameters each. All models are trained on sequences of 16k tokens and show improvements on inputs with up"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Code Llama reaches state-of-the-art performance among open models on several code benchmarks, with scores of up to 67% and 65% on HumanEval and MBPP, respectively. Notably, Code Llama - Python 7B outperforms Llama 2 70B on HumanEval and MBPP, and all our models outperform every other publicly available model on MultiPL-E.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the benchmark results (HumanEval, MBPP, MultiPL-E) reflect genuine generalization to real-world coding without significant test data contamination in the training corpus and that the evaluation protocols are comparable across all compared models.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Code Llama models achieve state-of-the-art performance among open models on HumanEval (up to 67%) and MBPP (up to 65%), with the 7B Python variant outperforming Llama 2 70B and all models beating others on MultiPL-E.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Code Llama models achieve state-of-the-art results among open models on code benchmarks while adding infilling and long-context support.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d96c987e9ec14e0eefb654edf54cdbc914b5490b887ee6409bf9fc22244bdc9a"},"source":{"id":"2308.12950","kind":"arxiv","version":3},"verdict":{"id":"86e1a744-a6e6-493f-8503-a11cc51fd46c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T15:01:53.308367Z","strongest_claim":"Code Llama reaches state-of-the-art performance among open models on several code benchmarks, with scores of up to 67% and 65% on HumanEval and MBPP, respectively. Notably, Code Llama - Python 7B outperforms Llama 2 70B on HumanEval and MBPP, and all our models outperform every other publicly available model on MultiPL-E.","one_line_summary":"Code Llama models achieve state-of-the-art performance among open models on HumanEval (up to 67%) and MBPP (up to 65%), with the 7B Python variant outperforming Llama 2 70B and all models beating others on MultiPL-E.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the benchmark results (HumanEval, MBPP, MultiPL-E) reflect genuine generalization to real-world coding without significant test data contamination in the training corpus and that the evaluation protocols are comparable across all compared models.","pith_extraction_headline":"Code Llama models achieve state-of-the-art results among open models on code benchmarks while adding infilling and long-context support."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2308.12950/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":21,"sample":[{"doi":"","year":null,"title":"LCC-balanced","work_id":"ad65e764-7efb-4e75-ad47-263bcdeed4fb","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Write a function that finds the maximum depth of list nesting in a given list","work_id":"6f27c5ee-9ef4-4947-a75a-53a7014bba9b","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Given an integer array nums, rotate the array to the right by k steps, where k is non-negative","work_id":"90bcc94d-94b2-4b60-ba6b-77fec2500cfe","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"A pitch should consist of a letter, possibly a # sign, and an octave number","work_id":"37fff9bd-d5b6-48fa-b244-d562488d3225","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Write a function that removes any sequences of whitespace that are between numbers in an input string","work_id":"5067d43a-01af-4b2e-9d8e-d0a0e3633636","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":21,"snapshot_sha256":"d631f9e58c216ab057f1091aa05cf8f42ae0a4a07392ce0bf414908c3a7d54fd","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"b07dadb8f1fbf575a46c5d8b4c95f50ae1a7a8f11867e94124ba30f82144cddc"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"86e1a744-a6e6-493f-8503-a11cc51fd46c"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:40:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pwlMXk1Zq3GtifowwYN7N6yDWwIeLSWVqxUHm3vtpNH02oc13HxMAwniJtPgeDe3hDAsPP31FMVCjK23Er+ADg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T19:05:27.025621Z"},"content_sha256":"324eb59a75fd2139fe0a1775a9f68f39a8c18b58d925e0971cfbd5ee2e75abf3","schema_version":"1.0","event_id":"sha256:324eb59a75fd2139fe0a1775a9f68f39a8c18b58d925e0971cfbd5ee2e75abf3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4HCXU2UBLE6ERE6JHYJQ4DYVAG/bundle.json","state_url":"https://pith.science/pith/4HCXU2UBLE6ERE6JHYJQ4DYVAG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4HCXU2UBLE6ERE6JHYJQ4DYVAG/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":"2026-08-12T19:05:27Z","links":{"resolver":"https://pith.science/pith/4HCXU2UBLE6ERE6JHYJQ4DYVAG","bundle":"https://pith.science/pith/4HCXU2UBLE6ERE6JHYJQ4DYVAG/bundle.json","state":"https://pith.science/pith/4HCXU2UBLE6ERE6JHYJQ4DYVAG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4HCXU2UBLE6ERE6JHYJQ4DYVAG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:4HCXU2UBLE6ERE6JHYJQ4DYVAG","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":"59de633ed50b5b1789c46fe99f056c009fccebfd1681b21fd272fa1b8c263cf5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-24T17:39:13Z","title_canon_sha256":"f24ce93deac952ca680c24617d26a3d0bde497229d6443aa52f4331fe2f66a2a"},"schema_version":"1.0","source":{"id":"2308.12950","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.12950","created_at":"2026-07-05T07:40:01Z"},{"alias_kind":"arxiv_version","alias_value":"2308.12950v3","created_at":"2026-07-05T07:40:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.12950","created_at":"2026-07-05T07:40:01Z"},{"alias_kind":"pith_short_12","alias_value":"4HCXU2UBLE6E","created_at":"2026-07-05T07:40:01Z"},{"alias_kind":"pith_short_16","alias_value":"4HCXU2UBLE6ERE6J","created_at":"2026-07-05T07:40:01Z"},{"alias_kind":"pith_short_8","alias_value":"4HCXU2UB","created_at":"2026-07-05T07:40:01Z"}],"graph_snapshots":[{"event_id":"sha256:324eb59a75fd2139fe0a1775a9f68f39a8c18b58d925e0971cfbd5ee2e75abf3","target":"graph","created_at":"2026-07-05T07:40:01Z","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":"Code Llama reaches state-of-the-art performance among open models on several code benchmarks, with scores of up to 67% and 65% on HumanEval and MBPP, respectively. Notably, Code Llama - Python 7B outperforms Llama 2 70B on HumanEval and MBPP, and all our models outperform every other publicly available model on MultiPL-E."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the benchmark results (HumanEval, MBPP, MultiPL-E) reflect genuine generalization to real-world coding without significant test data contamination in the training corpus and that the evaluation protocols are comparable across all compared models."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Code Llama models achieve state-of-the-art performance among open models on HumanEval (up to 67%) and MBPP (up to 65%), with the 7B Python variant outperforming Llama 2 70B and all models beating others on MultiPL-E."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Code Llama models achieve state-of-the-art results among open models on code benchmarks while adding infilling and long-context support."}],"snapshot_sha256":"d96c987e9ec14e0eefb654edf54cdbc914b5490b887ee6409bf9fc22244bdc9a"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"b07dadb8f1fbf575a46c5d8b4c95f50ae1a7a8f11867e94124ba30f82144cddc"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2308.12950/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama - Python), and instruction-following models (Code Llama - Instruct) with 7B, 13B, 34B and 70B parameters each. All models are trained on sequences of 16k tokens and show improvements on inputs with up","authors_text":"Aaron Grattafiori, Alexandre D\\'efossez, Artyom Kozhevnikov, Baptiste Rozi\\`ere, Cristian Canton Ferrer, Fabian Gloeckle, Faisal Azhar, Gabriel Synnaeve, Hugo Touvron, Itai Gat, Ivan Evtimov, Jade Copet, J\\'er\\'emy Rapin, Jingyu Liu, Joanna Bitton, Jonas Gehring, Louis Martin, Manish Bhatt, Nicolas Usunier, Romain Sauvestre, Sten Sootla, Tal Remez, Thomas Scialom, Wenhan Xiong, Xiaoqing Ellen Tan, Yossi Adi","cross_cats":[],"headline":"Code Llama models achieve state-of-the-art results among open models on code benchmarks while adding infilling and long-context support.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-24T17:39:13Z","title":"Code Llama: Open Foundation Models for Code"},"references":{"count":21,"internal_anchors":0,"resolved_work":21,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"LCC-balanced","work_id":"ad65e764-7efb-4e75-ad47-263bcdeed4fb","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Write a function that finds the maximum depth of list nesting in a given list","work_id":"6f27c5ee-9ef4-4947-a75a-53a7014bba9b","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Given an integer array nums, rotate the array to the right by k steps, where k is non-negative","work_id":"90bcc94d-94b2-4b60-ba6b-77fec2500cfe","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"A pitch should consist of a letter, possibly a # sign, and an octave number","work_id":"37fff9bd-d5b6-48fa-b244-d562488d3225","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Write a function that removes any sequences of whitespace that are between numbers in an input string","work_id":"5067d43a-01af-4b2e-9d8e-d0a0e3633636","year":null}],"snapshot_sha256":"d631f9e58c216ab057f1091aa05cf8f42ae0a4a07392ce0bf414908c3a7d54fd"},"source":{"id":"2308.12950","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-10T15:01:53.308367Z","id":"86e1a744-a6e6-493f-8503-a11cc51fd46c","model_set":{"reader":"grok-4.3"},"one_line_summary":"Code Llama models achieve state-of-the-art performance among open models on HumanEval (up to 67%) and MBPP (up to 65%), with the 7B Python variant outperforming Llama 2 70B and all models beating others on MultiPL-E.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Code Llama models achieve state-of-the-art results among open models on code benchmarks while adding infilling and long-context support.","strongest_claim":"Code Llama reaches state-of-the-art performance among open models on several code benchmarks, with scores of up to 67% and 65% on HumanEval and MBPP, respectively. Notably, Code Llama - Python 7B outperforms Llama 2 70B on HumanEval and MBPP, and all our models outperform every other publicly available model on MultiPL-E.","weakest_assumption":"That the benchmark results (HumanEval, MBPP, MultiPL-E) reflect genuine generalization to real-world coding without significant test data contamination in the training corpus and that the evaluation protocols are comparable across all compared models."}},"verdict_id":"86e1a744-a6e6-493f-8503-a11cc51fd46c"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5fea39dd111d3fb6c7edd0706962f2b61a0f695ecb5c5976c34a790b00bed119","target":"record","created_at":"2026-07-05T07:40:01Z","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":"59de633ed50b5b1789c46fe99f056c009fccebfd1681b21fd272fa1b8c263cf5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-24T17:39:13Z","title_canon_sha256":"f24ce93deac952ca680c24617d26a3d0bde497229d6443aa52f4331fe2f66a2a"},"schema_version":"1.0","source":{"id":"2308.12950","kind":"arxiv","version":3}},"canonical_sha256":"e1c57a6a81593c4893c93e130e0f15019701171acf55b42e94537386d9f2d0d5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1c57a6a81593c4893c93e130e0f15019701171acf55b42e94537386d9f2d0d5","first_computed_at":"2026-07-05T07:40:01.031250Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:40:01.031250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r2P/5R74aiY0/yT1L5BYgLTdjzg/TwK5Ou817D6XV6TcEM+2Jp2IZkLJWO1GQHViNHUT3/ffPEuywnYMZ0psDA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:40:01.031752Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.12950","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5fea39dd111d3fb6c7edd0706962f2b61a0f695ecb5c5976c34a790b00bed119","sha256:324eb59a75fd2139fe0a1775a9f68f39a8c18b58d925e0971cfbd5ee2e75abf3"],"state_sha256":"675d9cc0adec716a3fd213c9032c526b96895d7089fe852bb035437e84680f48"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PIhrITH7XGpx2UzfCxiqrV47eTGyV6DtcBNoDS4hQwnGymwI4eEJa5u2IDdnAqEhJRof2QRbIUJc/gZZ15xhDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T19:05:27.031415Z","bundle_sha256":"141174ec39924a1e99ba4caa6b9f609f94d790753ea5bbc76edd496bc05c19a0"}}