{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:CIHWLDKTKGCTCOIPMPEHC3WEPE","short_pith_number":"pith:CIHWLDKT","canonical_record":{"source":{"id":"2304.01373","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-04-03T20:58:15Z","cross_cats_sorted":[],"title_canon_sha256":"0ace9f335158bb7146489e81651673b6809aa20162fa00ad6de66d9a408ff3db","abstract_canon_sha256":"cb96737b1f74b472e0e4c6f7b934e1843aa0112ab33accf7b0435c9c26ebe4ce"},"schema_version":"1.0"},"canonical_sha256":"120f658d53518531390f63c8716ec479191aa9c9eb53d212b17f5f5f4bd0e183","source":{"kind":"arxiv","id":"2304.01373","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.01373","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"arxiv_version","alias_value":"2304.01373v2","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.01373","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"pith_short_12","alias_value":"CIHWLDKTKGCT","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"CIHWLDKTKGCTCOIP","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"CIHWLDKT","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:CIHWLDKTKGCTCOIPMPEHC3WEPE","target":"record","payload":{"canonical_record":{"source":{"id":"2304.01373","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-04-03T20:58:15Z","cross_cats_sorted":[],"title_canon_sha256":"0ace9f335158bb7146489e81651673b6809aa20162fa00ad6de66d9a408ff3db","abstract_canon_sha256":"cb96737b1f74b472e0e4c6f7b934e1843aa0112ab33accf7b0435c9c26ebe4ce"},"schema_version":"1.0"},"canonical_sha256":"120f658d53518531390f63c8716ec479191aa9c9eb53d212b17f5f5f4bd0e183","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:50.687412Z","signature_b64":"JGwXWmKkcLD1ujlKb7aBarfbY+JQ9rRuTVbmjxR/awVfc8Jb5/u8zbOYM4AXZk4UXhpwz2bXyB3s42IhaPdcCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"120f658d53518531390f63c8716ec479191aa9c9eb53d212b17f5f5f4bd0e183","last_reissued_at":"2026-05-17T23:38:50.686760Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:50.686760Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.01373","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":"2026-05-17T23:38:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3zXhvWJoIenp0deTB1vx5bikCjd7M9bCF+lqYyDkLcKUZpy0qT86wJFDgE1/UHZiqXQ0cq+JQUu3QglLk0VkDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:57:38.279680Z"},"content_sha256":"a6442d84df01fd4f1b7427800598d686444f97c5f1f5644d216e08c3b8c3f240","schema_version":"1.0","event_id":"sha256:a6442d84df01fd4f1b7427800598d686444f97c5f1f5644d216e08c3b8c3f240"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:CIHWLDKTKGCTCOIPMPEHC3WEPE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"A suite of 16 language models trained on identical public data in the same order from 70M to 12B parameters enables direct tracking of how abilities emerge during training and across scales.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aviya Skowron, Edward Raff, Eric Hallahan, Hailey Schoelkopf, Herbie Bradley, Kyle O'Brien, Lintang Sutawika, Mohammad Aflah Khan, Oskar van der Wal, Quentin Anthony, Shivanshu Purohit, Stella Biderman, USVSN Sai Prashanth","submitted_at":"2023-04-03T20:58:15Z","abstract_excerpt":"How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \\textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend \\textit{Pythia} to facilitate research in many areas, and we present several case studies including novel res"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We introduce Pythia, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That training all models on the exact same public data in identical order, combined with released checkpoints, will produce reproducible and generalizable insights into training dynamics without major unaccounted confounding from data selection or implementation details.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Pythia releases 16 identically trained LLMs with full checkpoints and data tools to study training dynamics, scaling, memorization, and bias in language models.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A suite of 16 language models trained on identical public data in the same order from 70M to 12B parameters enables direct tracking of how abilities emerge during training and across scales.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"83547dcd4e8a958e939ffff8dedf386c524724f13ea93f2bcbe1c828782a876b"},"source":{"id":"2304.01373","kind":"arxiv","version":2},"verdict":{"id":"e26e5881-5d37-4723-b9fd-accbad77771b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T17:39:56.487514Z","strongest_claim":"We introduce Pythia, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics.","one_line_summary":"Pythia releases 16 identically trained LLMs with full checkpoints and data tools to study training dynamics, scaling, memorization, and bias in language models.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That training all models on the exact same public data in identical order, combined with released checkpoints, will produce reproducible and generalizable insights into training dynamics without major unaccounted confounding from data selection or implementation details.","pith_extraction_headline":"A suite of 16 language models trained on identical public data in the same order from 70M to 12B parameters enables direct tracking of how abilities emerge during training and across scales."},"references":{"count":198,"sample":[{"doi":"","year":2022,"title":"J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., et al","work_id":"1662457e-d86b-4d61-b9b2-b0e7f45a1fde","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"GPT-NeoX : Large scale autoregressive language modeling in PyTorch , 8 2021","work_id":"b3387a01-83d4-450f-a38c-e5da600946f1","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N","work_id":"1ecb7ba1-5877-47bf-a9ba-6fb99718e7a5","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"S., Sutawika, L., Purohit, S., Schoelkopf, H., Anthony, Q., and Raff, E","work_id":"82be10dc-2bc8-48d8-b059-ef2354881ed7","ref_index":6,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"GPT-Neo : Large scale autoregressive language modeling with Mesh-TensorFlow","work_id":"b60b3a29-5cfa-4f5c-ae16-d8eddcde0dfa","ref_index":8,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":198,"snapshot_sha256":"9258ef03a0a53c74a34a5cf88a359acc06333b3edbed47318973f1e9136ac0f8","internal_anchors":27},"formal_canon":{"evidence_count":2,"snapshot_sha256":"e9be58d49d4d7cea3017eaac2aa9a5fe1520d4e23dfccb19040ab8b6bbe401e3"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"e26e5881-5d37-4723-b9fd-accbad77771b"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5P6Qj+1qsK3YrUdujDQwT05pRovOvZjG4PpuGcG3SUFBG2/Pi4U8xLfCVkNzbefoyyH6EUO2M3k/rnxMZD+dBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:57:38.280544Z"},"content_sha256":"dc0852c37fed2c967f8209d5a3514556c615679bd3ea2e09f758f08344d4ce5d","schema_version":"1.0","event_id":"sha256:dc0852c37fed2c967f8209d5a3514556c615679bd3ea2e09f758f08344d4ce5d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CIHWLDKTKGCTCOIPMPEHC3WEPE/bundle.json","state_url":"https://pith.science/pith/CIHWLDKTKGCTCOIPMPEHC3WEPE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CIHWLDKTKGCTCOIPMPEHC3WEPE/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":"2026-05-25T21:57:38Z","links":{"resolver":"https://pith.science/pith/CIHWLDKTKGCTCOIPMPEHC3WEPE","bundle":"https://pith.science/pith/CIHWLDKTKGCTCOIPMPEHC3WEPE/bundle.json","state":"https://pith.science/pith/CIHWLDKTKGCTCOIPMPEHC3WEPE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CIHWLDKTKGCTCOIPMPEHC3WEPE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:CIHWLDKTKGCTCOIPMPEHC3WEPE","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":"cb96737b1f74b472e0e4c6f7b934e1843aa0112ab33accf7b0435c9c26ebe4ce","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-04-03T20:58:15Z","title_canon_sha256":"0ace9f335158bb7146489e81651673b6809aa20162fa00ad6de66d9a408ff3db"},"schema_version":"1.0","source":{"id":"2304.01373","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.01373","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"arxiv_version","alias_value":"2304.01373v2","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.01373","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"pith_short_12","alias_value":"CIHWLDKTKGCT","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"CIHWLDKTKGCTCOIP","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"CIHWLDKT","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:dc0852c37fed2c967f8209d5a3514556c615679bd3ea2e09f758f08344d4ce5d","target":"graph","created_at":"2026-05-17T23:38:50Z","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":"We introduce Pythia, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That training all models on the exact same public data in identical order, combined with released checkpoints, will produce reproducible and generalizable insights into training dynamics without major unaccounted confounding from data selection or implementation details."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Pythia releases 16 identically trained LLMs with full checkpoints and data tools to study training dynamics, scaling, memorization, and bias in language models."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A suite of 16 language models trained on identical public data in the same order from 70M to 12B parameters enables direct tracking of how abilities emerge during training and across scales."}],"snapshot_sha256":"83547dcd4e8a958e939ffff8dedf386c524724f13ea93f2bcbe1c828782a876b"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"e9be58d49d4d7cea3017eaac2aa9a5fe1520d4e23dfccb19040ab8b6bbe401e3"},"paper":{"abstract_excerpt":"How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \\textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend \\textit{Pythia} to facilitate research in many areas, and we present several case studies including novel res","authors_text":"Aviya Skowron, Edward Raff, Eric Hallahan, Hailey Schoelkopf, Herbie Bradley, Kyle O'Brien, Lintang Sutawika, Mohammad Aflah Khan, Oskar van der Wal, Quentin Anthony, Shivanshu Purohit, Stella Biderman, USVSN Sai Prashanth","cross_cats":[],"headline":"A suite of 16 language models trained on identical public data in the same order from 70M to 12B parameters enables direct tracking of how abilities emerge during training and across scales.","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-04-03T20:58:15Z","title":"Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling"},"references":{"count":198,"internal_anchors":27,"resolved_work":198,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., et al","work_id":"1662457e-d86b-4d61-b9b2-b0e7f45a1fde","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"GPT-NeoX : Large scale autoregressive language modeling in PyTorch , 8 2021","work_id":"b3387a01-83d4-450f-a38c-e5da600946f1","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N","work_id":"1ecb7ba1-5877-47bf-a9ba-6fb99718e7a5","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":6,"title":"S., Sutawika, L., Purohit, S., Schoelkopf, H., Anthony, Q., and Raff, E","work_id":"82be10dc-2bc8-48d8-b059-ef2354881ed7","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":8,"title":"GPT-Neo : Large scale autoregressive language modeling with Mesh-TensorFlow","work_id":"b60b3a29-5cfa-4f5c-ae16-d8eddcde0dfa","year":2021}],"snapshot_sha256":"9258ef03a0a53c74a34a5cf88a359acc06333b3edbed47318973f1e9136ac0f8"},"source":{"id":"2304.01373","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-15T17:39:56.487514Z","id":"e26e5881-5d37-4723-b9fd-accbad77771b","model_set":{"reader":"grok-4.3"},"one_line_summary":"Pythia releases 16 identically trained LLMs with full checkpoints and data tools to study training dynamics, scaling, memorization, and bias in language models.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A suite of 16 language models trained on identical public data in the same order from 70M to 12B parameters enables direct tracking of how abilities emerge during training and across scales.","strongest_claim":"We introduce Pythia, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics.","weakest_assumption":"That training all models on the exact same public data in identical order, combined with released checkpoints, will produce reproducible and generalizable insights into training dynamics without major unaccounted confounding from data selection or implementation details."}},"verdict_id":"e26e5881-5d37-4723-b9fd-accbad77771b"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a6442d84df01fd4f1b7427800598d686444f97c5f1f5644d216e08c3b8c3f240","target":"record","created_at":"2026-05-17T23:38:50Z","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":"cb96737b1f74b472e0e4c6f7b934e1843aa0112ab33accf7b0435c9c26ebe4ce","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-04-03T20:58:15Z","title_canon_sha256":"0ace9f335158bb7146489e81651673b6809aa20162fa00ad6de66d9a408ff3db"},"schema_version":"1.0","source":{"id":"2304.01373","kind":"arxiv","version":2}},"canonical_sha256":"120f658d53518531390f63c8716ec479191aa9c9eb53d212b17f5f5f4bd0e183","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"120f658d53518531390f63c8716ec479191aa9c9eb53d212b17f5f5f4bd0e183","first_computed_at":"2026-05-17T23:38:50.686760Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:50.686760Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JGwXWmKkcLD1ujlKb7aBarfbY+JQ9rRuTVbmjxR/awVfc8Jb5/u8zbOYM4AXZk4UXhpwz2bXyB3s42IhaPdcCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:50.687412Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.01373","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a6442d84df01fd4f1b7427800598d686444f97c5f1f5644d216e08c3b8c3f240","sha256:dc0852c37fed2c967f8209d5a3514556c615679bd3ea2e09f758f08344d4ce5d"],"state_sha256":"0909c75567a6bd082776afb92ca7ef40ab989e63e2135fc1faaebf2ea8e9fa50"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9VZW4f3J9oQzt9GJl9lLGZBvAVTajIFMB2TVYFEbO7KmgcxcBzBSf1779ZZyLR3Buj4Agrx9KPvz8a/xMR3CCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:57:38.284147Z","bundle_sha256":"c95625d10eaaed829b38a227de26703eccbe8226bf8cdae8e5832a59976aba8e"}}