{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:L75IYTMPXLW4T46AI67766AXCL","short_pith_number":"pith:L75IYTMP","canonical_record":{"source":{"id":"2401.05561","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-10T22:07:21Z","cross_cats_sorted":[],"title_canon_sha256":"0bb44d579e2ced8b1af3c79995b0cfc06997002dd79d4622020c6c4a5db6951a","abstract_canon_sha256":"d5b35753e347d7b164d4af900cde92b333ec4916fe54b8343eb7cbd5ab487d12"},"schema_version":"1.0"},"canonical_sha256":"5ffa8c4d8fbaedc9f3c047bfff781712ef5c88414a2ca27b9fac6b167b05265a","source":{"kind":"arxiv","id":"2401.05561","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.05561","created_at":"2026-07-05T09:13:34Z"},{"alias_kind":"arxiv_version","alias_value":"2401.05561v6","created_at":"2026-07-05T09:13:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.05561","created_at":"2026-07-05T09:13:34Z"},{"alias_kind":"pith_short_12","alias_value":"L75IYTMPXLW4","created_at":"2026-07-05T09:13:34Z"},{"alias_kind":"pith_short_16","alias_value":"L75IYTMPXLW4T46A","created_at":"2026-07-05T09:13:34Z"},{"alias_kind":"pith_short_8","alias_value":"L75IYTMP","created_at":"2026-07-05T09:13:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:L75IYTMPXLW4T46AI67766AXCL","target":"record","payload":{"canonical_record":{"source":{"id":"2401.05561","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-10T22:07:21Z","cross_cats_sorted":[],"title_canon_sha256":"0bb44d579e2ced8b1af3c79995b0cfc06997002dd79d4622020c6c4a5db6951a","abstract_canon_sha256":"d5b35753e347d7b164d4af900cde92b333ec4916fe54b8343eb7cbd5ab487d12"},"schema_version":"1.0"},"canonical_sha256":"5ffa8c4d8fbaedc9f3c047bfff781712ef5c88414a2ca27b9fac6b167b05265a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:34.851669Z","signature_b64":"pEH3qxC4kjwWtUooXTXimBsY564zBj7lpa7Vf+jt8RGC2hHCjv4APxCLgtnFF/ibmUshdGbA8Jnmw8WW1q4SCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ffa8c4d8fbaedc9f3c047bfff781712ef5c88414a2ca27b9fac6b167b05265a","last_reissued_at":"2026-07-05T09:13:34.851102Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:34.851102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.05561","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:13:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OzL0NYS8fgnuStIDIPDK7FaS0X73MeJ2FUVOU3fwCYShq4/KrpywWewTfQqq1uMbkQmJ51ftbXlsiXCkNZJwBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T20:38:20.543979Z"},"content_sha256":"629ff6dcbc9f5251a1279a693ec94f2e40f7c114be928f29c85fc5bc5f255307","schema_version":"1.0","event_id":"sha256:629ff6dcbc9f5251a1279a693ec94f2e40f7c114be928f29c85fc5bc5f255307"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:L75IYTMPXLW4T46AI67766AXCL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TrustLLM: Trustworthiness in Large Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"Proprietary large language models generally outperform open-source ones on trustworthiness measures, and trustworthiness tracks closely with overall utility.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chujie Gao, Chunyuan Li, Eric Xing, Furong Huang, Hao Liu, Haoran Wang, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, James Zou, Jianfeng Gao, Jian Liu, Jian Pei, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John Mitchell, Kaidi Xu, Kai Shu, Kai-Wei Chang, Lichao Sun, Lifang He, Lifu Huang, Manolis Kellis, Marinka Zitnik, Meng Jiang, Michael Backes, Mohit Bansal, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Qihui Zhang, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Siyuan Wu, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, Wenhan Lyu, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xiner Li, Xing Xie, Xun Chen, Xuyu Wang, Yanfang Ye, Yan Liu, Yijue Wang, Yinzhi Cao, Yixin Huang, Yixin Liu, Yixuan Zhang, Yong Chen, Yuan Li, Yue Huang, Yue Zhao, Zhengliang Liu, Zhikun Zhang","submitted_at":"2024-01-10T22:07:21Z","abstract_excerpt":"Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Therefore, ensuring the trustworthiness of LLMs emerges as an important topic. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and f"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Proprietary LLMs generally outperform most open-source counterparts in trustworthiness, while trustworthiness and utility are positively related, and some models over-calibrate by refusing benign prompts.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The chosen datasets and evaluation protocols across the six dimensions accurately capture the real-world trustworthiness risks the principles aim to address, without significant gaps or biases in task selection.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"TrustLLM defines eight trustworthiness principles, creates a six-dimension benchmark, and evaluates 16 LLMs showing proprietary models generally lead but some open-source ones are close while over-calibration can hurt utility.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Proprietary large language models generally outperform open-source ones on trustworthiness measures, and trustworthiness tracks closely with overall utility.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"5b4352d90a3ec359a9207c3c834d24aa4f1fbf36f391ae105caa72772f02169e"},"source":{"id":"2401.05561","kind":"arxiv","version":6},"verdict":{"id":"c60be685-787c-45b9-9d98-2e948a9accca","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T11:12:58.510044Z","strongest_claim":"Proprietary LLMs generally outperform most open-source counterparts in trustworthiness, while trustworthiness and utility are positively related, and some models over-calibrate by refusing benign prompts.","one_line_summary":"TrustLLM defines eight trustworthiness principles, creates a six-dimension benchmark, and evaluates 16 LLMs showing proprietary models generally lead but some open-source ones are close while over-calibration can hurt utility.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The chosen datasets and evaluation protocols across the six dimensions accurately capture the real-world trustworthiness risks the principles aim to address, without significant gaps or biases in task selection.","pith_extraction_headline":"Proprietary large language models generally outperform open-source ones on trustworthiness measures, and trustworthiness tracks closely with overall utility."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.05561/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":299,"sample":[{"doi":"","year":2022,"title":"A toolkit for text extraction and analysis for natural language processing tasks","work_id":"81dc7cb0-7b2b-4843-a50a-ff5112cc11b6","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Natural language processing: State of the art, current trends and challenges","work_id":"2bc7f8bc-259c-4e08-9ce1-3385da63c74b","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Wordcraft: story writing with large language models","work_id":"bb152c56-5310-43f0-ba5b-6ef51b9ed164","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Multilingual machine translation with large language models: Empirical results and analysis","work_id":"ddea8193-7a4a-4f93-9eda-7d7902f62735","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"https://blogs.microsoft.com/blog/2023/02/07/ reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/","work_id":"d1ebe99a-4677-450a-8fca-264ab1ebb983","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":299,"snapshot_sha256":"b51d39e2581505fda6d013032d2833b1e672c3a54039fc5417fb9735dbec58cf","internal_anchors":34},"formal_canon":{"evidence_count":2,"snapshot_sha256":"b36c28552cf3b4f693e73fc14c4c91969495a42f8d4840dbfe98b68060fae176"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"c60be685-787c-45b9-9d98-2e948a9accca"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:13:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P7ZH4jda7ezbNcm7D62BvTi+JaPXAgn2anORHv2UmEHs/oMP4AvBhmNvVFJQHYBWeTOPkpZ/xodZ7XBektrBBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T20:38:20.545091Z"},"content_sha256":"99eb8806bfcb568d23554310faf612d5946518e0379151e8ff272a5bdbaaf8c6","schema_version":"1.0","event_id":"sha256:99eb8806bfcb568d23554310faf612d5946518e0379151e8ff272a5bdbaaf8c6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L75IYTMPXLW4T46AI67766AXCL/bundle.json","state_url":"https://pith.science/pith/L75IYTMPXLW4T46AI67766AXCL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L75IYTMPXLW4T46AI67766AXCL/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":"2026-08-04T20:38:20Z","links":{"resolver":"https://pith.science/pith/L75IYTMPXLW4T46AI67766AXCL","bundle":"https://pith.science/pith/L75IYTMPXLW4T46AI67766AXCL/bundle.json","state":"https://pith.science/pith/L75IYTMPXLW4T46AI67766AXCL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L75IYTMPXLW4T46AI67766AXCL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:L75IYTMPXLW4T46AI67766AXCL","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":"d5b35753e347d7b164d4af900cde92b333ec4916fe54b8343eb7cbd5ab487d12","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-10T22:07:21Z","title_canon_sha256":"0bb44d579e2ced8b1af3c79995b0cfc06997002dd79d4622020c6c4a5db6951a"},"schema_version":"1.0","source":{"id":"2401.05561","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.05561","created_at":"2026-07-05T09:13:34Z"},{"alias_kind":"arxiv_version","alias_value":"2401.05561v6","created_at":"2026-07-05T09:13:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.05561","created_at":"2026-07-05T09:13:34Z"},{"alias_kind":"pith_short_12","alias_value":"L75IYTMPXLW4","created_at":"2026-07-05T09:13:34Z"},{"alias_kind":"pith_short_16","alias_value":"L75IYTMPXLW4T46A","created_at":"2026-07-05T09:13:34Z"},{"alias_kind":"pith_short_8","alias_value":"L75IYTMP","created_at":"2026-07-05T09:13:34Z"}],"graph_snapshots":[{"event_id":"sha256:99eb8806bfcb568d23554310faf612d5946518e0379151e8ff272a5bdbaaf8c6","target":"graph","created_at":"2026-07-05T09:13:34Z","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":"Proprietary LLMs generally outperform most open-source counterparts in trustworthiness, while trustworthiness and utility are positively related, and some models over-calibrate by refusing benign prompts."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The chosen datasets and evaluation protocols across the six dimensions accurately capture the real-world trustworthiness risks the principles aim to address, without significant gaps or biases in task selection."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"TrustLLM defines eight trustworthiness principles, creates a six-dimension benchmark, and evaluates 16 LLMs showing proprietary models generally lead but some open-source ones are close while over-calibration can hurt utility."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Proprietary large language models generally outperform open-source ones on trustworthiness measures, and trustworthiness tracks closely with overall utility."}],"snapshot_sha256":"5b4352d90a3ec359a9207c3c834d24aa4f1fbf36f391ae105caa72772f02169e"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"b36c28552cf3b4f693e73fc14c4c91969495a42f8d4840dbfe98b68060fae176"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2401.05561/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Therefore, ensuring the trustworthiness of LLMs emerges as an important topic. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and f","authors_text":"Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chujie Gao, Chunyuan Li, Eric Xing, Furong Huang, Hao Liu, Haoran Wang, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, James Zou, Jianfeng Gao, Jian Liu, Jian Pei, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John Mitchell, Kaidi Xu, Kai Shu, Kai-Wei Chang, Lichao Sun, Lifang He, Lifu Huang, Manolis Kellis, Marinka Zitnik, Meng Jiang, Michael Backes, Mohit Bansal, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Qihui Zhang, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Siyuan Wu, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, Wenhan Lyu, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xiner Li, Xing Xie, Xun Chen, Xuyu Wang, Yanfang Ye, Yan Liu, Yijue Wang, Yinzhi Cao, Yixin Huang, Yixin Liu, Yixuan Zhang, Yong Chen, Yuan Li, Yue Huang, Yue Zhao, Zhengliang Liu, Zhikun Zhang","cross_cats":[],"headline":"Proprietary large language models generally outperform open-source ones on trustworthiness measures, and trustworthiness tracks closely with overall utility.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-10T22:07:21Z","title":"TrustLLM: Trustworthiness in Large Language Models"},"references":{"count":299,"internal_anchors":34,"resolved_work":299,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"A toolkit for text extraction and analysis for natural language processing tasks","work_id":"81dc7cb0-7b2b-4843-a50a-ff5112cc11b6","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Natural language processing: State of the art, current trends and challenges","work_id":"2bc7f8bc-259c-4e08-9ce1-3385da63c74b","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Wordcraft: story writing with large language models","work_id":"bb152c56-5310-43f0-ba5b-6ef51b9ed164","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Multilingual machine translation with large language models: Empirical results and analysis","work_id":"ddea8193-7a4a-4f93-9eda-7d7902f62735","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"https://blogs.microsoft.com/blog/2023/02/07/ reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/","work_id":"d1ebe99a-4677-450a-8fca-264ab1ebb983","year":2023}],"snapshot_sha256":"b51d39e2581505fda6d013032d2833b1e672c3a54039fc5417fb9735dbec58cf"},"source":{"id":"2401.05561","kind":"arxiv","version":6},"verdict":{"created_at":"2026-05-18T11:12:58.510044Z","id":"c60be685-787c-45b9-9d98-2e948a9accca","model_set":{"reader":"grok-4.3"},"one_line_summary":"TrustLLM defines eight trustworthiness principles, creates a six-dimension benchmark, and evaluates 16 LLMs showing proprietary models generally lead but some open-source ones are close while over-calibration can hurt utility.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Proprietary large language models generally outperform open-source ones on trustworthiness measures, and trustworthiness tracks closely with overall utility.","strongest_claim":"Proprietary LLMs generally outperform most open-source counterparts in trustworthiness, while trustworthiness and utility are positively related, and some models over-calibrate by refusing benign prompts.","weakest_assumption":"The chosen datasets and evaluation protocols across the six dimensions accurately capture the real-world trustworthiness risks the principles aim to address, without significant gaps or biases in task selection."}},"verdict_id":"c60be685-787c-45b9-9d98-2e948a9accca"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:629ff6dcbc9f5251a1279a693ec94f2e40f7c114be928f29c85fc5bc5f255307","target":"record","created_at":"2026-07-05T09:13:34Z","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":"d5b35753e347d7b164d4af900cde92b333ec4916fe54b8343eb7cbd5ab487d12","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-10T22:07:21Z","title_canon_sha256":"0bb44d579e2ced8b1af3c79995b0cfc06997002dd79d4622020c6c4a5db6951a"},"schema_version":"1.0","source":{"id":"2401.05561","kind":"arxiv","version":6}},"canonical_sha256":"5ffa8c4d8fbaedc9f3c047bfff781712ef5c88414a2ca27b9fac6b167b05265a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ffa8c4d8fbaedc9f3c047bfff781712ef5c88414a2ca27b9fac6b167b05265a","first_computed_at":"2026-07-05T09:13:34.851102Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:13:34.851102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pEH3qxC4kjwWtUooXTXimBsY564zBj7lpa7Vf+jt8RGC2hHCjv4APxCLgtnFF/ibmUshdGbA8Jnmw8WW1q4SCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:13:34.851669Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.05561","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:629ff6dcbc9f5251a1279a693ec94f2e40f7c114be928f29c85fc5bc5f255307","sha256:99eb8806bfcb568d23554310faf612d5946518e0379151e8ff272a5bdbaaf8c6"],"state_sha256":"f91f2f0429fdd67c05f54e1f16660f9b8b5309374ce1450c97a90ead90c8d8a8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ng1BAglfsyuDQ6GKAegLEew0Y1imDhwEsolMCz6kkN+1Ra7Ubb/NxDt5fR1qDGjzTxG66ITvmRIfBzECs/aPBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T20:38:20.553345Z","bundle_sha256":"0e236bf4c86b3ceb675f838f6227e7241a6d285bdde4a12b32ed0ca2a1796ec7"}}