{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7WQOLPRSPZ5PSHLGNMBPQTHAU7","short_pith_number":"pith:7WQOLPRS","schema_version":"1.0","canonical_sha256":"fda0e5be327e7af91d666b02f84ce0a7c52c397390040a045d0512013e6452c5","source":{"kind":"arxiv","id":"2404.03662","version":1},"attestation_state":"computed","paper":{"title":"X-lifecycle Learning for Cloud Incident Management using LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Aditya Singh, Anjaly Parayil, Chetan Bansal, Drishti Goel, Fiza Husain, Saravan Rajmohan, Supriyo Ghosh, Xuchao Zhang","submitted_at":"2024-02-15T06:19:02Z","abstract_excerpt":"Incident management for large cloud services is a complex and tedious process and requires significant amount of manual efforts from on-call engineers (OCEs). OCEs typically leverage data from different stages of the software development lifecycle [SDLC] (e.g., codes, configuration, monitor data, service properties, service dependencies, trouble-shooting documents, etc.) to generate insights for detection, root causing and mitigating of incidents. Recent advancements in large language models [LLMs] (e.g., ChatGPT, GPT-4, Gemini) created opportunities to automatically generate contextual recomm"},"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":"2404.03662","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-02-15T06:19:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"df88164a7ba820261d931c5782c7963ca8110dad32f507c3879e74f453232f8d","abstract_canon_sha256":"3df014ee3fe28ade9229608573b7a19152935627d158c965f5ee52c05c63a29e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:04:39.574985Z","signature_b64":"P3jFWaDMQhxDxymbW9F03/ajvAKrXfbhigbuw73R29/z/Xtw5Es1xqWfL96u1SY2tRUKJO1jv8eEvOYq2gZyBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fda0e5be327e7af91d666b02f84ce0a7c52c397390040a045d0512013e6452c5","last_reissued_at":"2026-07-05T08:04:39.574474Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:04:39.574474Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"X-lifecycle Learning for Cloud Incident Management using LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Aditya Singh, Anjaly Parayil, Chetan Bansal, Drishti Goel, Fiza Husain, Saravan Rajmohan, Supriyo Ghosh, Xuchao Zhang","submitted_at":"2024-02-15T06:19:02Z","abstract_excerpt":"Incident management for large cloud services is a complex and tedious process and requires significant amount of manual efforts from on-call engineers (OCEs). OCEs typically leverage data from different stages of the software development lifecycle [SDLC] (e.g., codes, configuration, monitor data, service properties, service dependencies, trouble-shooting documents, etc.) to generate insights for detection, root causing and mitigating of incidents. Recent advancements in large language models [LLMs] (e.g., ChatGPT, GPT-4, Gemini) created opportunities to automatically generate contextual recomm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.03662","kind":"arxiv","version":1},"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/2404.03662/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":"2404.03662","created_at":"2026-07-05T08:04:39.574534+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.03662v1","created_at":"2026-07-05T08:04:39.574534+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.03662","created_at":"2026-07-05T08:04:39.574534+00:00"},{"alias_kind":"pith_short_12","alias_value":"7WQOLPRSPZ5P","created_at":"2026-07-05T08:04:39.574534+00:00"},{"alias_kind":"pith_short_16","alias_value":"7WQOLPRSPZ5PSHLG","created_at":"2026-07-05T08:04:39.574534+00:00"},{"alias_kind":"pith_short_8","alias_value":"7WQOLPRS","created_at":"2026-07-05T08:04:39.574534+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.11094","citing_title":"E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7WQOLPRSPZ5PSHLGNMBPQTHAU7","json":"https://pith.science/pith/7WQOLPRSPZ5PSHLGNMBPQTHAU7.json","graph_json":"https://pith.science/api/pith-number/7WQOLPRSPZ5PSHLGNMBPQTHAU7/graph.json","events_json":"https://pith.science/api/pith-number/7WQOLPRSPZ5PSHLGNMBPQTHAU7/events.json","paper":"https://pith.science/paper/7WQOLPRS"},"agent_actions":{"view_html":"https://pith.science/pith/7WQOLPRSPZ5PSHLGNMBPQTHAU7","download_json":"https://pith.science/pith/7WQOLPRSPZ5PSHLGNMBPQTHAU7.json","view_paper":"https://pith.science/paper/7WQOLPRS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.03662&json=true","fetch_graph":"https://pith.science/api/pith-number/7WQOLPRSPZ5PSHLGNMBPQTHAU7/graph.json","fetch_events":"https://pith.science/api/pith-number/7WQOLPRSPZ5PSHLGNMBPQTHAU7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7WQOLPRSPZ5PSHLGNMBPQTHAU7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7WQOLPRSPZ5PSHLGNMBPQTHAU7/action/storage_attestation","attest_author":"https://pith.science/pith/7WQOLPRSPZ5PSHLGNMBPQTHAU7/action/author_attestation","sign_citation":"https://pith.science/pith/7WQOLPRSPZ5PSHLGNMBPQTHAU7/action/citation_signature","submit_replication":"https://pith.science/pith/7WQOLPRSPZ5PSHLGNMBPQTHAU7/action/replication_record"}},"created_at":"2026-07-05T08:04:39.574534+00:00","updated_at":"2026-07-05T08:04:39.574534+00:00"}