{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EP4WLZNG4KXSM32KEI2ZX6QNEB","short_pith_number":"pith:EP4WLZNG","schema_version":"1.0","canonical_sha256":"23f965e5a6e2af266f4a22359bfa0d2060094d4b4d90b6c1c6ddfbeadeec7a3b","source":{"kind":"arxiv","id":"2410.09352","version":2},"attestation_state":"computed","paper":{"title":"LogLM: From Task-based to Instruction-based Automated Log Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.SE","authors_text":"Boxing Chen, Hao Yang, Minggui He, Shenglin Zhang, Shimin Tao, Weibin Meng, Yilun Liu, Yongqian Sun, Yuhe Ji, Yuming Xie","submitted_at":"2024-10-12T03:36:52Z","abstract_excerpt":"Automatic log analysis is essential for the efficient Operation and Maintenance (O&M) of software systems, providing critical insights into system behaviors. However, existing approaches mostly treat log analysis as training a model to perform an isolated task ( e.g., anomaly detection, log parsing, etc.) using task-specific log-label pairs. These task-based approaches are inflexible in generalizing to complex scenarios, depend on task-specific training data, and cost significantly when deploying multiple models. In this paper, we propose an instruction-based training approach that transforms "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.09352","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-10-12T03:36:52Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"9960d8282068e7785eb3760cb52d84f02c543432cd68ab857ccc31978dc8e3b9","abstract_canon_sha256":"bbb74018ba65c2ba218b19ccb94fdb2d44e9a584ed018d86df13081eac9c8f56"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:59:00.434678Z","signature_b64":"NVhJkKUBcH/uYWLQQq0gVjz3lPptj5XmlXm/H+bp9GYTURiJ0SRjWI0wEk4iRa8nL7wVig2iq2e0jOJccfG2AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23f965e5a6e2af266f4a22359bfa0d2060094d4b4d90b6c1c6ddfbeadeec7a3b","last_reissued_at":"2026-07-05T09:59:00.434203Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:59:00.434203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LogLM: From Task-based to Instruction-based Automated Log Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.SE","authors_text":"Boxing Chen, Hao Yang, Minggui He, Shenglin Zhang, Shimin Tao, Weibin Meng, Yilun Liu, Yongqian Sun, Yuhe Ji, Yuming Xie","submitted_at":"2024-10-12T03:36:52Z","abstract_excerpt":"Automatic log analysis is essential for the efficient Operation and Maintenance (O&M) of software systems, providing critical insights into system behaviors. However, existing approaches mostly treat log analysis as training a model to perform an isolated task ( e.g., anomaly detection, log parsing, etc.) using task-specific log-label pairs. These task-based approaches are inflexible in generalizing to complex scenarios, depend on task-specific training data, and cost significantly when deploying multiple models. In this paper, we propose an instruction-based training approach that transforms "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09352","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.09352/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.09352","created_at":"2026-07-05T09:59:00.434261+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.09352v2","created_at":"2026-07-05T09:59:00.434261+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09352","created_at":"2026-07-05T09:59:00.434261+00:00"},{"alias_kind":"pith_short_12","alias_value":"EP4WLZNG4KXS","created_at":"2026-07-05T09:59:00.434261+00:00"},{"alias_kind":"pith_short_16","alias_value":"EP4WLZNG4KXSM32K","created_at":"2026-07-05T09:59:00.434261+00:00"},{"alias_kind":"pith_short_8","alias_value":"EP4WLZNG","created_at":"2026-07-05T09:59:00.434261+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.05785","citing_title":"Can Large Language Models Generate Observability-Aware Code?","ref_index":25,"is_internal_anchor":true},{"citing_arxiv_id":"2605.14866","citing_title":"Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought","ref_index":85,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11094","citing_title":"E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EP4WLZNG4KXSM32KEI2ZX6QNEB","json":"https://pith.science/pith/EP4WLZNG4KXSM32KEI2ZX6QNEB.json","graph_json":"https://pith.science/api/pith-number/EP4WLZNG4KXSM32KEI2ZX6QNEB/graph.json","events_json":"https://pith.science/api/pith-number/EP4WLZNG4KXSM32KEI2ZX6QNEB/events.json","paper":"https://pith.science/paper/EP4WLZNG"},"agent_actions":{"view_html":"https://pith.science/pith/EP4WLZNG4KXSM32KEI2ZX6QNEB","download_json":"https://pith.science/pith/EP4WLZNG4KXSM32KEI2ZX6QNEB.json","view_paper":"https://pith.science/paper/EP4WLZNG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.09352&json=true","fetch_graph":"https://pith.science/api/pith-number/EP4WLZNG4KXSM32KEI2ZX6QNEB/graph.json","fetch_events":"https://pith.science/api/pith-number/EP4WLZNG4KXSM32KEI2ZX6QNEB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EP4WLZNG4KXSM32KEI2ZX6QNEB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EP4WLZNG4KXSM32KEI2ZX6QNEB/action/storage_attestation","attest_author":"https://pith.science/pith/EP4WLZNG4KXSM32KEI2ZX6QNEB/action/author_attestation","sign_citation":"https://pith.science/pith/EP4WLZNG4KXSM32KEI2ZX6QNEB/action/citation_signature","submit_replication":"https://pith.science/pith/EP4WLZNG4KXSM32KEI2ZX6QNEB/action/replication_record"}},"created_at":"2026-07-05T09:59:00.434261+00:00","updated_at":"2026-07-05T09:59:00.434261+00:00"}