{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CWGBTCX5VPVVKCYFFIL34CLUZC","short_pith_number":"pith:CWGBTCX5","schema_version":"1.0","canonical_sha256":"158c198afdabeb550b052a17be0974c8ba3a956edfe6690fd198c42e17228040","source":{"kind":"arxiv","id":"2301.03797","version":2},"attestation_state":"computed","paper":{"title":"Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Chetan Bansal, Saravan Rajmohan, Supriyo Ghosh, Thomas Zimmermann, Toufique Ahmed, Xuchao Zhang","submitted_at":"2023-01-10T05:41:40Z","abstract_excerpt":"Incident management for cloud services is a complex process involving several steps and has a huge impact on both service health and developer productivity. On-call engineers require significant amount of domain knowledge and manual effort for root causing and mitigation of production incidents. Recent advances in artificial intelligence has resulted in state-of-the-art large language models like GPT-3.x (both GPT-3.0 and GPT-3.5), which have been used to solve a variety of problems ranging from question answering to text summarization. In this work, we do the first large-scale study to evalua"},"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":"2301.03797","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-01-10T05:41:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5974187321c2caf5f75859044dcdf87fc3a8a2255714e968980b8cda83ab5ffd","abstract_canon_sha256":"f5551d82ce39d9a3aed047f0773c2511e96d78735f06cec8416d9ee9e5bedf06"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:10.772399Z","signature_b64":"lAMsPQI7KDPvSh1E9uzcDYmrl8MuorPOHgsfPlz5+y3K61G+N24VdofrbzW8CGH092R9w531Q0Fa+KsrbdnWBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"158c198afdabeb550b052a17be0974c8ba3a956edfe6690fd198c42e17228040","last_reissued_at":"2026-07-05T05:40:10.772002Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:10.772002Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Chetan Bansal, Saravan Rajmohan, Supriyo Ghosh, Thomas Zimmermann, Toufique Ahmed, Xuchao Zhang","submitted_at":"2023-01-10T05:41:40Z","abstract_excerpt":"Incident management for cloud services is a complex process involving several steps and has a huge impact on both service health and developer productivity. On-call engineers require significant amount of domain knowledge and manual effort for root causing and mitigation of production incidents. Recent advances in artificial intelligence has resulted in state-of-the-art large language models like GPT-3.x (both GPT-3.0 and GPT-3.5), which have been used to solve a variety of problems ranging from question answering to text summarization. In this work, we do the first large-scale study to evalua"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.03797","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/2301.03797/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":"2301.03797","created_at":"2026-07-05T05:40:10.772052+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.03797v2","created_at":"2026-07-05T05:40:10.772052+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.03797","created_at":"2026-07-05T05:40:10.772052+00:00"},{"alias_kind":"pith_short_12","alias_value":"CWGBTCX5VPVV","created_at":"2026-07-05T05:40:10.772052+00:00"},{"alias_kind":"pith_short_16","alias_value":"CWGBTCX5VPVVKCYF","created_at":"2026-07-05T05:40:10.772052+00:00"},{"alias_kind":"pith_short_8","alias_value":"CWGBTCX5","created_at":"2026-07-05T05:40:10.772052+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CWGBTCX5VPVVKCYFFIL34CLUZC","json":"https://pith.science/pith/CWGBTCX5VPVVKCYFFIL34CLUZC.json","graph_json":"https://pith.science/api/pith-number/CWGBTCX5VPVVKCYFFIL34CLUZC/graph.json","events_json":"https://pith.science/api/pith-number/CWGBTCX5VPVVKCYFFIL34CLUZC/events.json","paper":"https://pith.science/paper/CWGBTCX5"},"agent_actions":{"view_html":"https://pith.science/pith/CWGBTCX5VPVVKCYFFIL34CLUZC","download_json":"https://pith.science/pith/CWGBTCX5VPVVKCYFFIL34CLUZC.json","view_paper":"https://pith.science/paper/CWGBTCX5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.03797&json=true","fetch_graph":"https://pith.science/api/pith-number/CWGBTCX5VPVVKCYFFIL34CLUZC/graph.json","fetch_events":"https://pith.science/api/pith-number/CWGBTCX5VPVVKCYFFIL34CLUZC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CWGBTCX5VPVVKCYFFIL34CLUZC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CWGBTCX5VPVVKCYFFIL34CLUZC/action/storage_attestation","attest_author":"https://pith.science/pith/CWGBTCX5VPVVKCYFFIL34CLUZC/action/author_attestation","sign_citation":"https://pith.science/pith/CWGBTCX5VPVVKCYFFIL34CLUZC/action/citation_signature","submit_replication":"https://pith.science/pith/CWGBTCX5VPVVKCYFFIL34CLUZC/action/replication_record"}},"created_at":"2026-07-05T05:40:10.772052+00:00","updated_at":"2026-07-05T05:40:10.772052+00:00"}