{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:4GRNZWZPMNCEH2VEYYDJZL7OUU","short_pith_number":"pith:4GRNZWZP","canonical_record":{"source":{"id":"2605.12978","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-13T04:15:50Z","cross_cats_sorted":[],"title_canon_sha256":"8d110404a9f0bd78130fa0f993d6c99bcd8b4b87b34ffa6f80d90d055fc4044e","abstract_canon_sha256":"2f7370ef81a575c6e0e9d975fcbb16718b09c785774323377c7f21157b6ab71a"},"schema_version":"1.0"},"canonical_sha256":"e1a2dcdb2f634443eaa4c6069cafeea53f8cc8a6f9e73c1fcbd16f4da22d3000","source":{"kind":"arxiv","id":"2605.12978","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12978","created_at":"2026-05-18T03:09:08Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12978v1","created_at":"2026-05-18T03:09:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12978","created_at":"2026-05-18T03:09:08Z"},{"alias_kind":"pith_short_12","alias_value":"4GRNZWZPMNCE","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"4GRNZWZPMNCEH2VE","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"4GRNZWZP","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:4GRNZWZPMNCEH2VEYYDJZL7OUU","target":"record","payload":{"canonical_record":{"source":{"id":"2605.12978","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-13T04:15:50Z","cross_cats_sorted":[],"title_canon_sha256":"8d110404a9f0bd78130fa0f993d6c99bcd8b4b87b34ffa6f80d90d055fc4044e","abstract_canon_sha256":"2f7370ef81a575c6e0e9d975fcbb16718b09c785774323377c7f21157b6ab71a"},"schema_version":"1.0"},"canonical_sha256":"e1a2dcdb2f634443eaa4c6069cafeea53f8cc8a6f9e73c1fcbd16f4da22d3000","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:09:08.679863Z","signature_b64":"VdPqXWLUoLmqScxqM9BN+jVioEv8I0sz17wvaq0+rhSm/l+q+5yk0AOizUDQ1TzGGdYBJkOvc/TsIR5nTXCWCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1a2dcdb2f634443eaa4c6069cafeea53f8cc8a6f9e73c1fcbd16f4da22d3000","last_reissued_at":"2026-05-18T03:09:08.679222Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:09:08.679222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.12978","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:09:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P4i95SDhdboCFCe6wBdNTv2/Cu7C7HGnn9mG79bzC25RqY2waqZJTrEJsVBshrFBi47lzYTcpeD8S66Q1ZUJBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T20:01:06.695013Z"},"content_sha256":"eea9a7b16060b12984b54a99ac432a8f83fa5c8935164b197280e15f64a0de9b","schema_version":"1.0","event_id":"sha256:eea9a7b16060b12984b54a99ac432a8f83fa5c8935164b197280e15f64a0de9b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:4GRNZWZPMNCEH2VEYYDJZL7OUU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Useful Memories Become Faulty When Continuously Updated by LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Consolidated memories from LLMs degrade over repeated updates and can perform worse than using no memory at all.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bingxuan Li, Dianqi Li, Dylan Zhang, Hao Peng, Yanshan Lin, Yihang Sun, Zhengkun Wu","submitted_at":"2026-05-13T04:15:50Z","abstract_excerpt":"Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated abstractions distilled across many episodes into reusable, schema-like lessons. Recent agentic-memory systems pursue the consolidated form: an LLM rewrites past trajectories into a textual memory bank that it continuously updates with new interactions, promising self-improving agents without parameter updates. Yet we find that such consolidated memories produced by today's LLMs are often faulty even when derived from useful experiences. As cons"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Consolidated memories produced by today's LLMs are often faulty even when derived from useful experiences. As consolidation proceeds, memory utility first rises, then degrades, and can fall below the no-memory baseline. More surprisingly, even when consolidating from ground-truth solutions, GPT-5.4 fails on 54% of a set of ARC-AGI problems it had previously solved without memory.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the observed degradation is caused by the consolidation step itself rather than by limitations specific to the tested models, tasks, or update schedules, and that the ARC-AGI Stream environment sufficiently represents real-world agent memory use.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"LLM-consolidated memories in agents degrade over continuous updates even from useful experiences, causing up to 54% failure on previously solved ARC-AGI problems, while episodic retention preserves accuracy.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Consolidated memories from LLMs degrade over repeated updates and can perform worse than using no memory at all.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"7a2d1b4cf8e8611f38624aaeaec705568f76cdad810c25524fb70f9bd1f3036c"},"source":{"id":"2605.12978","kind":"arxiv","version":1},"verdict":{"id":"37294837-d99e-4c75-b111-c076633e814c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T19:55:18.938103Z","strongest_claim":"Consolidated memories produced by today's LLMs are often faulty even when derived from useful experiences. As consolidation proceeds, memory utility first rises, then degrades, and can fall below the no-memory baseline. More surprisingly, even when consolidating from ground-truth solutions, GPT-5.4 fails on 54% of a set of ARC-AGI problems it had previously solved without memory.","one_line_summary":"LLM-consolidated memories in agents degrade over continuous updates even from useful experiences, causing up to 54% failure on previously solved ARC-AGI problems, while episodic retention preserves accuracy.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the observed degradation is caused by the consolidation step itself rather than by limitations specific to the tested models, tasks, or update schedules, and that the ARC-AGI Stream environment sufficiently represents real-world agent memory use.","pith_extraction_headline":"Consolidated memories from LLMs degrade over repeated updates and can perform worse than using no memory at all."},"references":{"count":15,"sample":[{"doi":"10.1145/3586183.3606763","year":2016,"title":"URLhttps://arxiv.org/abs/2511.00162. Morris Moscovitch, Roberto Cabeza, Gordon Winocur, and Lynn Nadel. Episodic memory and beyond: The hippocampus and neocortex in transformation.Annual Review of Psy","work_id":"b5bf85fe-4fb7-4966-b0b2-9ccf9d3b11b9","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"You may RETAIN entries by index, MERGE several into a cleaner entry, or DROP entries by omitting them from the output","work_id":"74ad0fe5-09d4-41c0-8a7a-a317c557a031","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"from_existing","work_id":"3ca905f6-4a57-4724-b2be-8811c86c14c6","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"When to use: The task has two same-sized input grids and the output has the same height but double the width, arranged as a left-right concatenation. The left half reproduces the shape pattern from th","work_id":"a2e258e5-061d-4f28-8bd5-cbea6ce00bbe","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"reason\" in your reply). You MUST pick one existing strategy -- no other action is accepted: B) **Use an existing strategy**: {","work_id":"51e1acea-5313-482f-830e-6b37b2cdad5a","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":15,"snapshot_sha256":"2876651a7b979671b5e7ee5ec270103ff09a39b4bec308dbea1e6cf8d1dae610","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"37294837-d99e-4c75-b111-c076633e814c"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:09:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7vKc1Sz/o1dzs7gC7BQUt8aMoPGYwEBm3aiRTJBVJNpFFgnOTrX1sl4an6vd34x63wTkArbduV+WELy7z6w3Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T20:01:06.695643Z"},"content_sha256":"f9d25a96f16ab5893d6c0f4279ec89783ec09eec4d79bfa08f8304f5a58c985c","schema_version":"1.0","event_id":"sha256:f9d25a96f16ab5893d6c0f4279ec89783ec09eec4d79bfa08f8304f5a58c985c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4GRNZWZPMNCEH2VEYYDJZL7OUU/bundle.json","state_url":"https://pith.science/pith/4GRNZWZPMNCEH2VEYYDJZL7OUU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4GRNZWZPMNCEH2VEYYDJZL7OUU/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":"2026-05-26T20:01:06Z","links":{"resolver":"https://pith.science/pith/4GRNZWZPMNCEH2VEYYDJZL7OUU","bundle":"https://pith.science/pith/4GRNZWZPMNCEH2VEYYDJZL7OUU/bundle.json","state":"https://pith.science/pith/4GRNZWZPMNCEH2VEYYDJZL7OUU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4GRNZWZPMNCEH2VEYYDJZL7OUU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4GRNZWZPMNCEH2VEYYDJZL7OUU","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":"2f7370ef81a575c6e0e9d975fcbb16718b09c785774323377c7f21157b6ab71a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-13T04:15:50Z","title_canon_sha256":"8d110404a9f0bd78130fa0f993d6c99bcd8b4b87b34ffa6f80d90d055fc4044e"},"schema_version":"1.0","source":{"id":"2605.12978","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12978","created_at":"2026-05-18T03:09:08Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12978v1","created_at":"2026-05-18T03:09:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12978","created_at":"2026-05-18T03:09:08Z"},{"alias_kind":"pith_short_12","alias_value":"4GRNZWZPMNCE","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"4GRNZWZPMNCEH2VE","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"4GRNZWZP","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:f9d25a96f16ab5893d6c0f4279ec89783ec09eec4d79bfa08f8304f5a58c985c","target":"graph","created_at":"2026-05-18T03:09:08Z","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":"Consolidated memories produced by today's LLMs are often faulty even when derived from useful experiences. As consolidation proceeds, memory utility first rises, then degrades, and can fall below the no-memory baseline. More surprisingly, even when consolidating from ground-truth solutions, GPT-5.4 fails on 54% of a set of ARC-AGI problems it had previously solved without memory."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the observed degradation is caused by the consolidation step itself rather than by limitations specific to the tested models, tasks, or update schedules, and that the ARC-AGI Stream environment sufficiently represents real-world agent memory use."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"LLM-consolidated memories in agents degrade over continuous updates even from useful experiences, causing up to 54% failure on previously solved ARC-AGI problems, while episodic retention preserves accuracy."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Consolidated memories from LLMs degrade over repeated updates and can perform worse than using no memory at all."}],"snapshot_sha256":"7a2d1b4cf8e8611f38624aaeaec705568f76cdad810c25524fb70f9bd1f3036c"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated abstractions distilled across many episodes into reusable, schema-like lessons. Recent agentic-memory systems pursue the consolidated form: an LLM rewrites past trajectories into a textual memory bank that it continuously updates with new interactions, promising self-improving agents without parameter updates. Yet we find that such consolidated memories produced by today's LLMs are often faulty even when derived from useful experiences. As cons","authors_text":"Bingxuan Li, Dianqi Li, Dylan Zhang, Hao Peng, Yanshan Lin, Yihang Sun, Zhengkun Wu","cross_cats":[],"headline":"Consolidated memories from LLMs degrade over repeated updates and can perform worse than using no memory at all.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-13T04:15:50Z","title":"Useful Memories Become Faulty When Continuously Updated by LLMs"},"references":{"count":15,"internal_anchors":0,"resolved_work":15,"sample":[{"cited_arxiv_id":"","doi":"10.1145/3586183.3606763","is_internal_anchor":false,"ref_index":1,"title":"URLhttps://arxiv.org/abs/2511.00162. Morris Moscovitch, Roberto Cabeza, Gordon Winocur, and Lynn Nadel. Episodic memory and beyond: The hippocampus and neocortex in transformation.Annual Review of Psy","work_id":"b5bf85fe-4fb7-4966-b0b2-9ccf9d3b11b9","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"You may RETAIN entries by index, MERGE several into a cleaner entry, or DROP entries by omitting them from the output","work_id":"74ad0fe5-09d4-41c0-8a7a-a317c557a031","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"from_existing","work_id":"3ca905f6-4a57-4724-b2be-8811c86c14c6","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"When to use: The task has two same-sized input grids and the output has the same height but double the width, arranged as a left-right concatenation. The left half reproduces the shape pattern from th","work_id":"a2e258e5-061d-4f28-8bd5-cbea6ce00bbe","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"reason\" in your reply). You MUST pick one existing strategy -- no other action is accepted: B) **Use an existing strategy**: {","work_id":"51e1acea-5313-482f-830e-6b37b2cdad5a","year":2024}],"snapshot_sha256":"2876651a7b979671b5e7ee5ec270103ff09a39b4bec308dbea1e6cf8d1dae610"},"source":{"id":"2605.12978","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T19:55:18.938103Z","id":"37294837-d99e-4c75-b111-c076633e814c","model_set":{"reader":"grok-4.3"},"one_line_summary":"LLM-consolidated memories in agents degrade over continuous updates even from useful experiences, causing up to 54% failure on previously solved ARC-AGI problems, while episodic retention preserves accuracy.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Consolidated memories from LLMs degrade over repeated updates and can perform worse than using no memory at all.","strongest_claim":"Consolidated memories produced by today's LLMs are often faulty even when derived from useful experiences. As consolidation proceeds, memory utility first rises, then degrades, and can fall below the no-memory baseline. More surprisingly, even when consolidating from ground-truth solutions, GPT-5.4 fails on 54% of a set of ARC-AGI problems it had previously solved without memory.","weakest_assumption":"That the observed degradation is caused by the consolidation step itself rather than by limitations specific to the tested models, tasks, or update schedules, and that the ARC-AGI Stream environment sufficiently represents real-world agent memory use."}},"verdict_id":"37294837-d99e-4c75-b111-c076633e814c"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:eea9a7b16060b12984b54a99ac432a8f83fa5c8935164b197280e15f64a0de9b","target":"record","created_at":"2026-05-18T03:09:08Z","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":"2f7370ef81a575c6e0e9d975fcbb16718b09c785774323377c7f21157b6ab71a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-13T04:15:50Z","title_canon_sha256":"8d110404a9f0bd78130fa0f993d6c99bcd8b4b87b34ffa6f80d90d055fc4044e"},"schema_version":"1.0","source":{"id":"2605.12978","kind":"arxiv","version":1}},"canonical_sha256":"e1a2dcdb2f634443eaa4c6069cafeea53f8cc8a6f9e73c1fcbd16f4da22d3000","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1a2dcdb2f634443eaa4c6069cafeea53f8cc8a6f9e73c1fcbd16f4da22d3000","first_computed_at":"2026-05-18T03:09:08.679222Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:08.679222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VdPqXWLUoLmqScxqM9BN+jVioEv8I0sz17wvaq0+rhSm/l+q+5yk0AOizUDQ1TzGGdYBJkOvc/TsIR5nTXCWCA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:08.679863Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12978","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eea9a7b16060b12984b54a99ac432a8f83fa5c8935164b197280e15f64a0de9b","sha256:f9d25a96f16ab5893d6c0f4279ec89783ec09eec4d79bfa08f8304f5a58c985c"],"state_sha256":"0bf4e4aa71f5610a190e93ed1d041345226fcba1eae8f56fe218252ac9bde9db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4aTCzTi+xgyucS/bvuR9SWGTeINDglBixsc2GIIBrBRxhguW154f3AiIFkc0UEtmYcBFlUylp9wIbHx6jGpYDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T20:01:06.699603Z","bundle_sha256":"ece825c53ac09644976fbe7d127aab5d466f17b03ad9e2bad1ddadc00f21b06e"}}