{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:XYLV35VY6NBT4YXFZ2TPKB5L6H","short_pith_number":"pith:XYLV35VY","canonical_record":{"source":{"id":"2109.01652","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-03T17:55:52Z","cross_cats_sorted":[],"title_canon_sha256":"cd2a5ca0bf6865f8a24d9a74a2b4a7983c993a16c729c07916d2184dcfe60aea","abstract_canon_sha256":"9af4b1ce5af050d1e20663a57a409d84e7334581daad938cf92e76fd83f33818"},"schema_version":"1.0"},"canonical_sha256":"be175df6b8f3433e62e5cea6f507abf1c80df707465f7123dbe22ee795b7d98d","source":{"kind":"arxiv","id":"2109.01652","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.01652","created_at":"2026-07-05T03:55:29Z"},{"alias_kind":"arxiv_version","alias_value":"2109.01652v5","created_at":"2026-07-05T03:55:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.01652","created_at":"2026-07-05T03:55:29Z"},{"alias_kind":"pith_short_12","alias_value":"XYLV35VY6NBT","created_at":"2026-07-05T03:55:29Z"},{"alias_kind":"pith_short_16","alias_value":"XYLV35VY6NBT4YXF","created_at":"2026-07-05T03:55:29Z"},{"alias_kind":"pith_short_8","alias_value":"XYLV35VY","created_at":"2026-07-05T03:55:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:XYLV35VY6NBT4YXFZ2TPKB5L6H","target":"record","payload":{"canonical_record":{"source":{"id":"2109.01652","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-03T17:55:52Z","cross_cats_sorted":[],"title_canon_sha256":"cd2a5ca0bf6865f8a24d9a74a2b4a7983c993a16c729c07916d2184dcfe60aea","abstract_canon_sha256":"9af4b1ce5af050d1e20663a57a409d84e7334581daad938cf92e76fd83f33818"},"schema_version":"1.0"},"canonical_sha256":"be175df6b8f3433e62e5cea6f507abf1c80df707465f7123dbe22ee795b7d98d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:55:29.549892Z","signature_b64":"tkgkFcpX7PH740LJ3r9RX+BbJqEbhWLXkbfyE0eI4arZD/Qpie2GCoRSgjtHg/tU/vztpYOlWP7431Hs6DgcAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be175df6b8f3433e62e5cea6f507abf1c80df707465f7123dbe22ee795b7d98d","last_reissued_at":"2026-07-05T03:55:29.549385Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:55:29.549385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.01652","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:55:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5Bg8zJpnpcGbdqLNtf/ooA125T4SnN/9DSKrjz7UTPtPodqN7g0U7qRgmvgHM+zdRtWQDeUbx3+wCWRfyB6eAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T05:49:56.304884Z"},"content_sha256":"c33d52856ccf45e4f00882d27ccc20f16e01171873547a733f026ecb8616a118","schema_version":"1.0","event_id":"sha256:c33d52856ccf45e4f00882d27ccc20f16e01171873547a733f026ecb8616a118"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:XYLV35VY6NBT4YXFZ2TPKB5L6H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Finetuned Language Models Are Zero-Shot Learners","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Finetuning language models on tasks described by instructions improves zero-shot performance on unseen tasks.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Adams Wei Yu, Andrew M. Dai, Brian Lester, Jason Wei, Kelvin Guu, Maarten Bosma, Nan Du, Quoc V. Le, Vincent Y. Zhao","submitted_at":"2021-09-03T17:55:52Z","abstract_excerpt":"This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks.\n  We take a 137B parameter pretrained language model and instruction-tune it on over 60 NLP tasks verbalized via natural language instruction templates. We evaluate this instruction-tuned model, which we call FLAN, on unseen task types. FLAN substantially improves the performance of its unmodified counterpart and sur"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 tasks that we evaluate.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the 25 evaluation tasks are truly unseen and representative, with no overlap or leakage from the 60+ finetuning tasks, and that gains come specifically from the instruction format rather than scale or data volume alone.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Instruction tuning a 137B language model on over 60 NLP tasks described by instructions substantially boosts zero-shot performance on unseen tasks, outperforming larger GPT-3 models.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Finetuning language models on tasks described by instructions improves zero-shot performance on unseen tasks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6ccd35339d552f667daf16b15d7dac69f20d0c6dfce4f4724b9e1fc7881e31cd"},"source":{"id":"2109.01652","kind":"arxiv","version":5},"verdict":{"id":"b1c362b3-afb1-432e-9b40-6c2256c6451a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T21:08:44.849352Z","strongest_claim":"instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 tasks that we evaluate.","one_line_summary":"Instruction tuning a 137B language model on over 60 NLP tasks described by instructions substantially boosts zero-shot performance on unseen tasks, outperforming larger GPT-3 models.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the 25 evaluation tasks are truly unseen and representative, with no overlap or leakage from the 60+ finetuning tasks, and that gains come specifically from the instruction format rather than scale or data volume alone.","pith_extraction_headline":"Finetuning language models on tasks described by instructions improves zero-shot performance on unseen tasks."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.01652/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":67,"sample":[{"doi":"","year":2020,"title":"Kenton Lee, Ming-Wei Chang, and Kristina Toutanova","work_id":"c673de46-8390-445a-889c-567858b01235","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Brian Lester, Rami Al-Rfou, and Noah Constant","work_id":"550ef658-fc47-4b1e-a687-a7684f5370d1","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"The Power of Scale for Parameter-Efficient Prompt Tuning","work_id":"1056ba8e-7b3f-4811-be8e-9a3ed9269acb","ref_index":3,"cited_arxiv_id":"2104.08691","is_internal_anchor":true},{"doi":"10.5555/3031843.3031909","year":2017,"title":"The W inograd S chema C hallenge","work_id":"58cfe4e8-0536-4322-a0a2-a2887a9da09d","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"URL https://aclanthology.org/N16-1098. 17 Published as a conference paper at ICLR 2022 Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas,","work_id":"87e0a6d5-614c-4833-976e-f075e6027e64","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":67,"snapshot_sha256":"ef4a748a647b61ec92e9e29d357100af1866128e890f34d945f416b3bf18debb","internal_anchors":3},"formal_canon":{"evidence_count":1,"snapshot_sha256":"dda3ad141dc79a7ae0179cddfed5913391b74ffcebaa1eaca39d1c86e770daf2"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"b1c362b3-afb1-432e-9b40-6c2256c6451a"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:55:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Uv1W50swK8V8fsHiuTTCqjqRK7QBgB5oRzc8kxXiMuWmAWctPUR88mCpTtLyMtbJGLsjfV87KZyebDSi67n2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T05:49:56.308507Z"},"content_sha256":"52ee6820a13f5556fe7355b328b38d57ba70915e53106dd6cd5cdfdbe114e778","schema_version":"1.0","event_id":"sha256:52ee6820a13f5556fe7355b328b38d57ba70915e53106dd6cd5cdfdbe114e778"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XYLV35VY6NBT4YXFZ2TPKB5L6H/bundle.json","state_url":"https://pith.science/pith/XYLV35VY6NBT4YXFZ2TPKB5L6H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XYLV35VY6NBT4YXFZ2TPKB5L6H/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":"2026-08-07T05:49:56Z","links":{"resolver":"https://pith.science/pith/XYLV35VY6NBT4YXFZ2TPKB5L6H","bundle":"https://pith.science/pith/XYLV35VY6NBT4YXFZ2TPKB5L6H/bundle.json","state":"https://pith.science/pith/XYLV35VY6NBT4YXFZ2TPKB5L6H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XYLV35VY6NBT4YXFZ2TPKB5L6H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:XYLV35VY6NBT4YXFZ2TPKB5L6H","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":"9af4b1ce5af050d1e20663a57a409d84e7334581daad938cf92e76fd83f33818","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-03T17:55:52Z","title_canon_sha256":"cd2a5ca0bf6865f8a24d9a74a2b4a7983c993a16c729c07916d2184dcfe60aea"},"schema_version":"1.0","source":{"id":"2109.01652","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.01652","created_at":"2026-07-05T03:55:29Z"},{"alias_kind":"arxiv_version","alias_value":"2109.01652v5","created_at":"2026-07-05T03:55:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.01652","created_at":"2026-07-05T03:55:29Z"},{"alias_kind":"pith_short_12","alias_value":"XYLV35VY6NBT","created_at":"2026-07-05T03:55:29Z"},{"alias_kind":"pith_short_16","alias_value":"XYLV35VY6NBT4YXF","created_at":"2026-07-05T03:55:29Z"},{"alias_kind":"pith_short_8","alias_value":"XYLV35VY","created_at":"2026-07-05T03:55:29Z"}],"graph_snapshots":[{"event_id":"sha256:52ee6820a13f5556fe7355b328b38d57ba70915e53106dd6cd5cdfdbe114e778","target":"graph","created_at":"2026-07-05T03:55:29Z","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":"instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 tasks that we evaluate."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the 25 evaluation tasks are truly unseen and representative, with no overlap or leakage from the 60+ finetuning tasks, and that gains come specifically from the instruction format rather than scale or data volume alone."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Instruction tuning a 137B language model on over 60 NLP tasks described by instructions substantially boosts zero-shot performance on unseen tasks, outperforming larger GPT-3 models."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Finetuning language models on tasks described by instructions improves zero-shot performance on unseen tasks."}],"snapshot_sha256":"6ccd35339d552f667daf16b15d7dac69f20d0c6dfce4f4724b9e1fc7881e31cd"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"dda3ad141dc79a7ae0179cddfed5913391b74ffcebaa1eaca39d1c86e770daf2"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2109.01652/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks.\n  We take a 137B parameter pretrained language model and instruction-tune it on over 60 NLP tasks verbalized via natural language instruction templates. We evaluate this instruction-tuned model, which we call FLAN, on unseen task types. FLAN substantially improves the performance of its unmodified counterpart and sur","authors_text":"Adams Wei Yu, Andrew M. Dai, Brian Lester, Jason Wei, Kelvin Guu, Maarten Bosma, Nan Du, Quoc V. Le, Vincent Y. Zhao","cross_cats":[],"headline":"Finetuning language models on tasks described by instructions improves zero-shot performance on unseen tasks.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-03T17:55:52Z","title":"Finetuned Language Models Are Zero-Shot Learners"},"references":{"count":67,"internal_anchors":3,"resolved_work":67,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Kenton Lee, Ming-Wei Chang, and Kristina Toutanova","work_id":"c673de46-8390-445a-889c-567858b01235","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Brian Lester, Rami Al-Rfou, and Noah Constant","work_id":"550ef658-fc47-4b1e-a687-a7684f5370d1","year":null},{"cited_arxiv_id":"2104.08691","doi":"","is_internal_anchor":true,"ref_index":3,"title":"The Power of Scale for Parameter-Efficient Prompt Tuning","work_id":"1056ba8e-7b3f-4811-be8e-9a3ed9269acb","year":null},{"cited_arxiv_id":"","doi":"10.5555/3031843.3031909","is_internal_anchor":false,"ref_index":4,"title":"The W inograd S chema C hallenge","work_id":"58cfe4e8-0536-4322-a0a2-a2887a9da09d","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"URL https://aclanthology.org/N16-1098. 17 Published as a conference paper at ICLR 2022 Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas,","work_id":"87e0a6d5-614c-4833-976e-f075e6027e64","year":2022}],"snapshot_sha256":"ef4a748a647b61ec92e9e29d357100af1866128e890f34d945f416b3bf18debb"},"source":{"id":"2109.01652","kind":"arxiv","version":5},"verdict":{"created_at":"2026-05-10T21:08:44.849352Z","id":"b1c362b3-afb1-432e-9b40-6c2256c6451a","model_set":{"reader":"grok-4.3"},"one_line_summary":"Instruction tuning a 137B language model on over 60 NLP tasks described by instructions substantially boosts zero-shot performance on unseen tasks, outperforming larger GPT-3 models.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Finetuning language models on tasks described by instructions improves zero-shot performance on unseen tasks.","strongest_claim":"instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 tasks that we evaluate.","weakest_assumption":"That the 25 evaluation tasks are truly unseen and representative, with no overlap or leakage from the 60+ finetuning tasks, and that gains come specifically from the instruction format rather than scale or data volume alone."}},"verdict_id":"b1c362b3-afb1-432e-9b40-6c2256c6451a"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c33d52856ccf45e4f00882d27ccc20f16e01171873547a733f026ecb8616a118","target":"record","created_at":"2026-07-05T03:55:29Z","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":"9af4b1ce5af050d1e20663a57a409d84e7334581daad938cf92e76fd83f33818","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-03T17:55:52Z","title_canon_sha256":"cd2a5ca0bf6865f8a24d9a74a2b4a7983c993a16c729c07916d2184dcfe60aea"},"schema_version":"1.0","source":{"id":"2109.01652","kind":"arxiv","version":5}},"canonical_sha256":"be175df6b8f3433e62e5cea6f507abf1c80df707465f7123dbe22ee795b7d98d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be175df6b8f3433e62e5cea6f507abf1c80df707465f7123dbe22ee795b7d98d","first_computed_at":"2026-07-05T03:55:29.549385Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:55:29.549385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tkgkFcpX7PH740LJ3r9RX+BbJqEbhWLXkbfyE0eI4arZD/Qpie2GCoRSgjtHg/tU/vztpYOlWP7431Hs6DgcAw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:55:29.549892Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.01652","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c33d52856ccf45e4f00882d27ccc20f16e01171873547a733f026ecb8616a118","sha256:52ee6820a13f5556fe7355b328b38d57ba70915e53106dd6cd5cdfdbe114e778"],"state_sha256":"8f52366ca8a141ca8d484588224b400c90c15f91906b2241450532cb3e6a5610"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lTWdChQquwVdq6XMV8hajhXoUQC85Q+qtZSyWICiKqrVo6yidT0fWKKTwoamTAYlLTiTO5JKGKzaH3XVRs72Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T05:49:56.317796Z","bundle_sha256":"4cac0321ace6bb8634bc1ca9d13aaa5cec11ee7f18e240e91c90180e87300773"}}