{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EA6WI3SHCXJVMELL4XHDJE6D7C","short_pith_number":"pith:EA6WI3SH","schema_version":"1.0","canonical_sha256":"203d646e4715d356116be5ce3493c3f8bc348b003927f658265f0bf4aac45ae0","source":{"kind":"arxiv","id":"2503.21833","version":1},"attestation_state":"computed","paper":{"title":"Refining Time Series Anomaly Detectors using Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alan Yang, Sean Lee, Venus Montes, Yulin Chen","submitted_at":"2025-03-26T23:41:49Z","abstract_excerpt":"Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal large language models (LLMs) to partially automate this process. We find that LLMs can effectively identify false alarms by integrating visual inspection of time series plots with text descriptions of the data-generating process. By leveragin"},"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":"2503.21833","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-26T23:41:49Z","cross_cats_sorted":[],"title_canon_sha256":"0cb80885c24d3448a2eb2155c26090ddc75f7b00825e36135aca582586edf541","abstract_canon_sha256":"75f5f1853285d9c9a6a3174817dd029740b7378ea00bf2a32d536bcefa91ec55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:40:42.945959Z","signature_b64":"rWUJULyKMV4dhoRXea9IloryRaI4LLzqpalO2+Uv4+AUzUWrdlOYgmbBtuiM/i2lLhEufTrftXAvnkDH+E57DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"203d646e4715d356116be5ce3493c3f8bc348b003927f658265f0bf4aac45ae0","last_reissued_at":"2026-07-05T10:40:42.945509Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:40:42.945509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Refining Time Series Anomaly Detectors using Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alan Yang, Sean Lee, Venus Montes, Yulin Chen","submitted_at":"2025-03-26T23:41:49Z","abstract_excerpt":"Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal large language models (LLMs) to partially automate this process. We find that LLMs can effectively identify false alarms by integrating visual inspection of time series plots with text descriptions of the data-generating process. By leveragin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.21833","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/2503.21833/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":"2503.21833","created_at":"2026-07-05T10:40:42.945561+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.21833v1","created_at":"2026-07-05T10:40:42.945561+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.21833","created_at":"2026-07-05T10:40:42.945561+00:00"},{"alias_kind":"pith_short_12","alias_value":"EA6WI3SHCXJV","created_at":"2026-07-05T10:40:42.945561+00:00"},{"alias_kind":"pith_short_16","alias_value":"EA6WI3SHCXJVMELL","created_at":"2026-07-05T10:40:42.945561+00:00"},{"alias_kind":"pith_short_8","alias_value":"EA6WI3SH","created_at":"2026-07-05T10:40:42.945561+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.14504","citing_title":"PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EA6WI3SHCXJVMELL4XHDJE6D7C","json":"https://pith.science/pith/EA6WI3SHCXJVMELL4XHDJE6D7C.json","graph_json":"https://pith.science/api/pith-number/EA6WI3SHCXJVMELL4XHDJE6D7C/graph.json","events_json":"https://pith.science/api/pith-number/EA6WI3SHCXJVMELL4XHDJE6D7C/events.json","paper":"https://pith.science/paper/EA6WI3SH"},"agent_actions":{"view_html":"https://pith.science/pith/EA6WI3SHCXJVMELL4XHDJE6D7C","download_json":"https://pith.science/pith/EA6WI3SHCXJVMELL4XHDJE6D7C.json","view_paper":"https://pith.science/paper/EA6WI3SH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.21833&json=true","fetch_graph":"https://pith.science/api/pith-number/EA6WI3SHCXJVMELL4XHDJE6D7C/graph.json","fetch_events":"https://pith.science/api/pith-number/EA6WI3SHCXJVMELL4XHDJE6D7C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EA6WI3SHCXJVMELL4XHDJE6D7C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EA6WI3SHCXJVMELL4XHDJE6D7C/action/storage_attestation","attest_author":"https://pith.science/pith/EA6WI3SHCXJVMELL4XHDJE6D7C/action/author_attestation","sign_citation":"https://pith.science/pith/EA6WI3SHCXJVMELL4XHDJE6D7C/action/citation_signature","submit_replication":"https://pith.science/pith/EA6WI3SHCXJVMELL4XHDJE6D7C/action/replication_record"}},"created_at":"2026-07-05T10:40:42.945561+00:00","updated_at":"2026-07-05T10:40:42.945561+00:00"}