{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ZT36ZURYVNS6KTXYQRG5W76P6Z","short_pith_number":"pith:ZT36ZURY","canonical_record":{"source":{"id":"2605.15208","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-02T05:41:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"34b49b2ebd2b1d00530c2b0b4ad637c1b06d8c8855e4b36ceb8a4092d8109107","abstract_canon_sha256":"24afd4b460284b160b3b5ae120d6d9dd44fcf4dc9e5ad1ec19b9dd39a5c7eb2a"},"schema_version":"1.0"},"canonical_sha256":"ccf7ecd238ab65e54ef8844ddb7fcff6623832065bb2cd6f0d2aa7747dfb30ee","source":{"kind":"arxiv","id":"2605.15208","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15208","created_at":"2026-05-20T00:00:46Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15208v1","created_at":"2026-05-20T00:00:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15208","created_at":"2026-05-20T00:00:46Z"},{"alias_kind":"pith_short_12","alias_value":"ZT36ZURYVNS6","created_at":"2026-05-20T00:00:46Z"},{"alias_kind":"pith_short_16","alias_value":"ZT36ZURYVNS6KTXY","created_at":"2026-05-20T00:00:46Z"},{"alias_kind":"pith_short_8","alias_value":"ZT36ZURY","created_at":"2026-05-20T00:00:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ZT36ZURYVNS6KTXYQRG5W76P6Z","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15208","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-02T05:41:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"34b49b2ebd2b1d00530c2b0b4ad637c1b06d8c8855e4b36ceb8a4092d8109107","abstract_canon_sha256":"24afd4b460284b160b3b5ae120d6d9dd44fcf4dc9e5ad1ec19b9dd39a5c7eb2a"},"schema_version":"1.0"},"canonical_sha256":"ccf7ecd238ab65e54ef8844ddb7fcff6623832065bb2cd6f0d2aa7747dfb30ee","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:00:46.289745Z","signature_b64":"/iiWBUgkS2QNWKnR0Pt80UIH6Wxz1G7f/cGp3GHhTPOL3tdnrL1p60yKlt73GdF5vnVqgX0exSGFoOYD/F71DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ccf7ecd238ab65e54ef8844ddb7fcff6623832065bb2cd6f0d2aa7747dfb30ee","last_reissued_at":"2026-05-20T00:00:46.288712Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:00:46.288712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15208","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":"2026-05-20T00:00:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GQufVhdwy9miupF0UNe3vPqp22m9aLZ1uY4lD5GVjD8TGNanFYcT+30aki5PAeT2bsO3xUlrZJ4PTOJfijiSAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:44:35.456245Z"},"content_sha256":"87b5abd777d1e0635723123c45e4c079bd59808e9627a4d3d380c0c67026e24b","schema_version":"1.0","event_id":"sha256:87b5abd777d1e0635723123c45e4c079bd59808e9627a4d3d380c0c67026e24b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ZT36ZURYVNS6KTXYQRG5W76P6Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Quantization at 3 bits causes 6-21% of unbiased LLM items to develop new stereotypes while perplexity barely changes.","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Plawan Kumar Rath, Rahul Maliakkal","submitted_at":"2026-05-02T05:41:47Z","abstract_excerpt":"Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deployment, yet the impact of this compression on model quality remains poorly understood. Existing studies typically compare only two conditions (full-precision vs. a single quantized variant), rely on aggregate bias metrics, and evaluate a single model family, making it impossible to distinguish gradual degradation from threshold-dependent safety failures. We conduct a controlled empirical study of three instruction-tuned models (Qwen2.5-7B, Mistral-"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"3-bit quantization causes 6-21% of previously unbiased items to develop new stereotypical behaviors, following a clear dose-response pattern confirmed via logistic regression, while models' willingness to select 'unknown' answers declines by 17.4%.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The BBQ benchmark items provide a valid and stable measure of stereotypical bias, and observed response changes at lower precisions reflect genuine bias emergence rather than random variation, model degradation artifacts, or evaluation noise; this premise underpins the interpretation of item-level shifts as fairness-critical failures.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"3-bit quantization induces new stereotypical biases in 6-21% of previously unbiased BBQ items across three LLMs, undetected by perplexity increases under 3%, with models declining in 'unknown' responses by 17.4%.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Quantization at 3 bits causes 6-21% of unbiased LLM items to develop new stereotypes while perplexity barely changes.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4b348e9c6d8046642a16c50ee350d9908eed9e055f23078c93e7c1e29ff9fb25"},"source":{"id":"2605.15208","kind":"arxiv","version":1},"verdict":{"id":"09ef77d1-262a-41dd-87d0-ecdb714480b9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T17:50:48.954164Z","strongest_claim":"3-bit quantization causes 6-21% of previously unbiased items to develop new stereotypical behaviors, following a clear dose-response pattern confirmed via logistic regression, while models' willingness to select 'unknown' answers declines by 17.4%.","one_line_summary":"3-bit quantization induces new stereotypical biases in 6-21% of previously unbiased BBQ items across three LLMs, undetected by perplexity increases under 3%, with models declining in 'unknown' responses by 17.4%.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The BBQ benchmark items provide a valid and stable measure of stereotypical bias, and observed response changes at lower precisions reflect genuine bias emergence rather than random variation, model degradation artifacts, or evaluation noise; this premise underpins the interpretation of item-level shifts as fairness-critical failures.","pith_extraction_headline":"Quantization at 3 bits causes 6-21% of unbiased LLM items to develop new stereotypes while perplexity barely changes."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15208/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T18:01:24.861394Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"8d946cbd69d86d94d9d0452a710124411b5827fb2fedf4c13b42b903750f0d02"},"references":{"count":25,"sample":[{"doi":"","year":2024,"title":"Large Language Models: A Survey","work_id":"54e385fe-1786-48c3-8aa0-d727210eb50e","ref_index":1,"cited_arxiv_id":"2402.06196","is_internal_anchor":true},{"doi":"","year":2025,"title":"A survey of post-training scaling in large language models,","work_id":"398e275e-11e0-42ea-92cd-8105ad843d81","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"LLMCBench: Benchmarking large language model com- pression for efficient deployment,","work_id":"f9b50f1c-e835-4a44-8a53-649fe1e3e8f9","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"A survey of model compression techniques: Past, present, and future,","work_id":"b8cbcbd5-4c00-490a-922b-ace6c7cfeee9","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions","work_id":"6389ea5a-8e37-498f-9bb2-a238ae4b3b9d","ref_index":5,"cited_arxiv_id":"2311.05232","is_internal_anchor":true}],"resolved_work":25,"snapshot_sha256":"a9758e5869378bd0435621b87f05745841175230c1ed88daae2f02805f4a7fbd","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"09e8b1b9daecaced6ce7861fb9215eaff4e6168e7b51502968c7301a2946762d"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"09ef77d1-262a-41dd-87d0-ecdb714480b9"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:00:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a1Ji17hRAtTHf6iDQfOg9/faCNQKQHjSw5ba+exopaP5DSUHx40uwetUmQNdaJPbsqOE51mvqTqBic0+7ZS5Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:44:35.457375Z"},"content_sha256":"eba3cb7a913a4d6da2bb523edb4575186acf25b590ccc316a6cf0f96d2bee33b","schema_version":"1.0","event_id":"sha256:eba3cb7a913a4d6da2bb523edb4575186acf25b590ccc316a6cf0f96d2bee33b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZT36ZURYVNS6KTXYQRG5W76P6Z/bundle.json","state_url":"https://pith.science/pith/ZT36ZURYVNS6KTXYQRG5W76P6Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZT36ZURYVNS6KTXYQRG5W76P6Z/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":"2026-05-26T03:44:35Z","links":{"resolver":"https://pith.science/pith/ZT36ZURYVNS6KTXYQRG5W76P6Z","bundle":"https://pith.science/pith/ZT36ZURYVNS6KTXYQRG5W76P6Z/bundle.json","state":"https://pith.science/pith/ZT36ZURYVNS6KTXYQRG5W76P6Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZT36ZURYVNS6KTXYQRG5W76P6Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZT36ZURYVNS6KTXYQRG5W76P6Z","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":"24afd4b460284b160b3b5ae120d6d9dd44fcf4dc9e5ad1ec19b9dd39a5c7eb2a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-02T05:41:47Z","title_canon_sha256":"34b49b2ebd2b1d00530c2b0b4ad637c1b06d8c8855e4b36ceb8a4092d8109107"},"schema_version":"1.0","source":{"id":"2605.15208","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15208","created_at":"2026-05-20T00:00:46Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15208v1","created_at":"2026-05-20T00:00:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15208","created_at":"2026-05-20T00:00:46Z"},{"alias_kind":"pith_short_12","alias_value":"ZT36ZURYVNS6","created_at":"2026-05-20T00:00:46Z"},{"alias_kind":"pith_short_16","alias_value":"ZT36ZURYVNS6KTXY","created_at":"2026-05-20T00:00:46Z"},{"alias_kind":"pith_short_8","alias_value":"ZT36ZURY","created_at":"2026-05-20T00:00:46Z"}],"graph_snapshots":[{"event_id":"sha256:eba3cb7a913a4d6da2bb523edb4575186acf25b590ccc316a6cf0f96d2bee33b","target":"graph","created_at":"2026-05-20T00:00:46Z","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":"3-bit quantization causes 6-21% of previously unbiased items to develop new stereotypical behaviors, following a clear dose-response pattern confirmed via logistic regression, while models' willingness to select 'unknown' answers declines by 17.4%."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The BBQ benchmark items provide a valid and stable measure of stereotypical bias, and observed response changes at lower precisions reflect genuine bias emergence rather than random variation, model degradation artifacts, or evaluation noise; this premise underpins the interpretation of item-level shifts as fairness-critical failures."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"3-bit quantization induces new stereotypical biases in 6-21% of previously unbiased BBQ items across three LLMs, undetected by perplexity increases under 3%, with models declining in 'unknown' responses by 17.4%."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Quantization at 3 bits causes 6-21% of unbiased LLM items to develop new stereotypes while perplexity barely changes."}],"snapshot_sha256":"4b348e9c6d8046642a16c50ee350d9908eed9e055f23078c93e7c1e29ff9fb25"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"09e8b1b9daecaced6ce7861fb9215eaff4e6168e7b51502968c7301a2946762d"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T18:01:24.861394Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.15208/integrity.json","findings":[],"snapshot_sha256":"8d946cbd69d86d94d9d0452a710124411b5827fb2fedf4c13b42b903750f0d02","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deployment, yet the impact of this compression on model quality remains poorly understood. Existing studies typically compare only two conditions (full-precision vs. a single quantized variant), rely on aggregate bias metrics, and evaluate a single model family, making it impossible to distinguish gradual degradation from threshold-dependent safety failures. We conduct a controlled empirical study of three instruction-tuned models (Qwen2.5-7B, Mistral-","authors_text":"Plawan Kumar Rath, Rahul Maliakkal","cross_cats":["cs.AI"],"headline":"Quantization at 3 bits causes 6-21% of unbiased LLM items to develop new stereotypes while perplexity barely changes.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-02T05:41:47Z","title":"Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels"},"references":{"count":25,"internal_anchors":2,"resolved_work":25,"sample":[{"cited_arxiv_id":"2402.06196","doi":"","is_internal_anchor":true,"ref_index":1,"title":"Large Language Models: A Survey","work_id":"54e385fe-1786-48c3-8aa0-d727210eb50e","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"A survey of post-training scaling in large language models,","work_id":"398e275e-11e0-42ea-92cd-8105ad843d81","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"LLMCBench: Benchmarking large language model com- pression for efficient deployment,","work_id":"f9b50f1c-e835-4a44-8a53-649fe1e3e8f9","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"A survey of model compression techniques: Past, present, and future,","work_id":"b8cbcbd5-4c00-490a-922b-ace6c7cfeee9","year":2025},{"cited_arxiv_id":"2311.05232","doi":"","is_internal_anchor":true,"ref_index":5,"title":"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions","work_id":"6389ea5a-8e37-498f-9bb2-a238ae4b3b9d","year":2023}],"snapshot_sha256":"a9758e5869378bd0435621b87f05745841175230c1ed88daae2f02805f4a7fbd"},"source":{"id":"2605.15208","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T17:50:48.954164Z","id":"09ef77d1-262a-41dd-87d0-ecdb714480b9","model_set":{"reader":"grok-4.3"},"one_line_summary":"3-bit quantization induces new stereotypical biases in 6-21% of previously unbiased BBQ items across three LLMs, undetected by perplexity increases under 3%, with models declining in 'unknown' responses by 17.4%.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Quantization at 3 bits causes 6-21% of unbiased LLM items to develop new stereotypes while perplexity barely changes.","strongest_claim":"3-bit quantization causes 6-21% of previously unbiased items to develop new stereotypical behaviors, following a clear dose-response pattern confirmed via logistic regression, while models' willingness to select 'unknown' answers declines by 17.4%.","weakest_assumption":"The BBQ benchmark items provide a valid and stable measure of stereotypical bias, and observed response changes at lower precisions reflect genuine bias emergence rather than random variation, model degradation artifacts, or evaluation noise; this premise underpins the interpretation of item-level shifts as fairness-critical failures."}},"verdict_id":"09ef77d1-262a-41dd-87d0-ecdb714480b9"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:87b5abd777d1e0635723123c45e4c079bd59808e9627a4d3d380c0c67026e24b","target":"record","created_at":"2026-05-20T00:00:46Z","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":"24afd4b460284b160b3b5ae120d6d9dd44fcf4dc9e5ad1ec19b9dd39a5c7eb2a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-02T05:41:47Z","title_canon_sha256":"34b49b2ebd2b1d00530c2b0b4ad637c1b06d8c8855e4b36ceb8a4092d8109107"},"schema_version":"1.0","source":{"id":"2605.15208","kind":"arxiv","version":1}},"canonical_sha256":"ccf7ecd238ab65e54ef8844ddb7fcff6623832065bb2cd6f0d2aa7747dfb30ee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ccf7ecd238ab65e54ef8844ddb7fcff6623832065bb2cd6f0d2aa7747dfb30ee","first_computed_at":"2026-05-20T00:00:46.288712Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:00:46.288712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/iiWBUgkS2QNWKnR0Pt80UIH6Wxz1G7f/cGp3GHhTPOL3tdnrL1p60yKlt73GdF5vnVqgX0exSGFoOYD/F71DQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:00:46.289745Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15208","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:87b5abd777d1e0635723123c45e4c079bd59808e9627a4d3d380c0c67026e24b","sha256:eba3cb7a913a4d6da2bb523edb4575186acf25b590ccc316a6cf0f96d2bee33b"],"state_sha256":"7707fc0e7b7a3c52947f3004dc6d6b938003ea9044cb7c387cd461c69108bb26"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+9u7VLOIfv7y8c9iBKjmOpyZKhJlPG74jxSyrlsTlmAQHSWfGcWGk5LYOvECP+vOiEK3EkkfWITtJhHGkUSaAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T03:44:35.461616Z","bundle_sha256":"29ce4f371a83b82f917027e0d834256c773ae60f02b2fb4edb760bb40b318646"}}