{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ECBE6AS7PDVGJOC5DDXMHYS7U5","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":"7a5c90ef2c5e2f9dd02409c63d74c9ba1a0495c32c72bbcd60fbb06f9898283d","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-02T02:39:33Z","title_canon_sha256":"4ee42421ec81fef7991631c7cc1e7a41a6df339cd7adb35901383182e1918393"},"schema_version":"1.0","source":{"id":"2407.01896","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.01896","created_at":"2026-07-05T08:39:06Z"},{"alias_kind":"arxiv_version","alias_value":"2407.01896v1","created_at":"2026-07-05T08:39:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.01896","created_at":"2026-07-05T08:39:06Z"},{"alias_kind":"pith_short_12","alias_value":"ECBE6AS7PDVG","created_at":"2026-07-05T08:39:06Z"},{"alias_kind":"pith_short_16","alias_value":"ECBE6AS7PDVGJOC5","created_at":"2026-07-05T08:39:06Z"},{"alias_kind":"pith_short_8","alias_value":"ECBE6AS7","created_at":"2026-07-05T08:39:06Z"}],"graph_snapshots":[{"event_id":"sha256:d628d8b9d449fe979cd499cc36274081a5cbacaa934f35b8b55d7882da9bd84e","target":"graph","created_at":"2026-07-05T08:39:06Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2407.01896/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Log analysis is crucial for ensuring the orderly and stable operation of information systems, particularly in the field of Artificial Intelligence for IT Operations (AIOps). Large Language Models (LLMs) have demonstrated significant potential in natural language processing tasks. In the AIOps domain, they excel in tasks such as anomaly detection, root cause analysis of faults, operations and maintenance script generation, and alert information summarization. However, the performance of current LLMs in log analysis tasks remains inadequately validated. To address this gap, we introduce LogEval,","authors_text":"Changchang Liu, Dan Pei, Duoming Lin, Shenglin Zhang, Shimin Tao, Shiyu Ma, Tianyu Cui, Tong Xiao, Weibin Meng, Yilun Liu, Yongqian Sun, Yuzhe Cai, Ziang Chen","cross_cats":["cs.IR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-02T02:39:33Z","title":"LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.01896","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2c95218dabd6ad3c80ff92acc7d648e4da650fcd63fe01fd91fcfb0099ed64a8","target":"record","created_at":"2026-07-05T08:39:06Z","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":"7a5c90ef2c5e2f9dd02409c63d74c9ba1a0495c32c72bbcd60fbb06f9898283d","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-02T02:39:33Z","title_canon_sha256":"4ee42421ec81fef7991631c7cc1e7a41a6df339cd7adb35901383182e1918393"},"schema_version":"1.0","source":{"id":"2407.01896","kind":"arxiv","version":1}},"canonical_sha256":"20824f025f78ea64b85d18eec3e25fa77c99c38ba808ce80274efdff8ffdf99c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"20824f025f78ea64b85d18eec3e25fa77c99c38ba808ce80274efdff8ffdf99c","first_computed_at":"2026-07-05T08:39:06.918510Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:39:06.918510Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jPsnVmoQSnwVUQLX7AfXm33TuuPsxC1Z33Zmj5sdNLipvYqN+8ErwG5MgFT1m+VE8ldsG+nRsPeR/NktkrEDDg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:39:06.919013Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.01896","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2c95218dabd6ad3c80ff92acc7d648e4da650fcd63fe01fd91fcfb0099ed64a8","sha256:d628d8b9d449fe979cd499cc36274081a5cbacaa934f35b8b55d7882da9bd84e"],"state_sha256":"0d698942990b14b80f618a31e10d38ac91aa83732e3bd90ab468233c0612484b"}