{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4YZWJ3NDD4KW5DD7IFVMSXB6CA","short_pith_number":"pith:4YZWJ3ND","schema_version":"1.0","canonical_sha256":"e63364eda31f156e8c7f416ac95c3e103509d5b718673580f5d00f967148599d","source":{"kind":"arxiv","id":"2605.04902","version":3},"attestation_state":"computed","paper":{"title":"AegisTS: A Hierarchical Agent System with Reinforcement Learning for Multivariate Time Series Data Cleaning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A hierarchical reinforcement learning agent system can jointly optimize the processing order and method selection to clean multiple quality issues in multivariate time series data without needing ground truth.","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Lu Chen, Mourad Khayati, Tianyi Li, Yuanyuan Yao, Yuhan Shi","submitted_at":"2026-05-06T13:31:37Z","abstract_excerpt":"Multivariate time series (MTS) are frequently affected by co-occurring quality issues, such as missing values, outliers, and constraint violations, which significantly undermine downstream analytics. Existing cleaning approaches fix only a limited set of such issues, making them ill-suited for scenarios where multiple quality problems arise simultaneously. Furthermore, these methods commonly depend on the availability of ground truth data or domain-specific rules, both of which are rarely accessible in real-world applications.\n  In this paper, we introduce AegisTS, an agent system with reinfor"},"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":"2605.04902","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2026-05-06T13:31:37Z","cross_cats_sorted":[],"title_canon_sha256":"d103d8a16c2c309530115cba0b4eff95c7a024902b9e44ace77449bf2f9b351f","abstract_canon_sha256":"a76b2863222eca801eac7edc2e4f4ce375b7980f682cc78a8d30b2ec6eeb0231"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:04:16.502727Z","signature_b64":"dDsKBOdSBs2Qg/eWO6tcppPJ/cunW05ykf7qd6Xe0XXaNnN7S58PtJ8ySt2ZLVHyG3n6ShJBtbtvzph902S5Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e63364eda31f156e8c7f416ac95c3e103509d5b718673580f5d00f967148599d","last_reissued_at":"2026-06-02T01:04:16.502178Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:04:16.502178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AegisTS: A Hierarchical Agent System with Reinforcement Learning for Multivariate Time Series Data Cleaning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A hierarchical reinforcement learning agent system can jointly optimize the processing order and method selection to clean multiple quality issues in multivariate time series data without needing ground truth.","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Lu Chen, Mourad Khayati, Tianyi Li, Yuanyuan Yao, Yuhan Shi","submitted_at":"2026-05-06T13:31:37Z","abstract_excerpt":"Multivariate time series (MTS) are frequently affected by co-occurring quality issues, such as missing values, outliers, and constraint violations, which significantly undermine downstream analytics. Existing cleaning approaches fix only a limited set of such issues, making them ill-suited for scenarios where multiple quality problems arise simultaneously. Furthermore, these methods commonly depend on the availability of ground truth data or domain-specific rules, both of which are rarely accessible in real-world applications.\n  In this paper, we introduce AegisTS, an agent system with reinfor"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our experimental results show that AegisTS consistently outperforms existing methods, achieving up to 96% improvement in data cleaning quality and 27% improvement in downstream performance.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the proposed dual-stage reward mechanism, which couples upstream cleaning quality with downstream task performance, can reliably guide the hierarchical agents to optimal pipelines in the absence of ground truth data.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"AegisTS uses a two-level RL agent architecture with a dual-stage reward to jointly optimize cleaning order and method selection for multivariate time series, delivering up to 96% better cleaning quality and 27% better downstream performance without ground truth.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A hierarchical reinforcement learning agent system can jointly optimize the processing order and method selection to clean multiple quality issues in multivariate time series data without needing ground truth.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4f5f82bb40717c7614ffcfde57febc050132e3c21505ced589cd2b9758e9dde1"},"source":{"id":"2605.04902","kind":"arxiv","version":3},"verdict":{"id":"d0cef379-a393-4ef4-9089-ba7c95790684","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T16:21:01.387848Z","strongest_claim":"Our experimental results show that AegisTS consistently outperforms existing methods, achieving up to 96% improvement in data cleaning quality and 27% improvement in downstream performance.","one_line_summary":"AegisTS uses a two-level RL agent architecture with a dual-stage reward to jointly optimize cleaning order and method selection for multivariate time series, delivering up to 96% better cleaning quality and 27% better downstream performance without ground truth.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the proposed dual-stage reward mechanism, which couples upstream cleaning quality with downstream task performance, can reliably guide the hierarchical agents to optimal pipelines in the absence of ground truth data.","pith_extraction_headline":"A hierarchical reinforcement learning agent system can jointly optimize the processing order and method selection to clean multiple quality issues in multivariate time series data without needing ground truth."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.04902/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T10:41:00.159384Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:23.702943Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T14:03:43.831112Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"4424964b1041675537bdecce4eecd371dd85fafd526b7a27c7179d95a6bf0089"},"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":"2605.04902","created_at":"2026-06-02T01:04:16.502244+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.04902v3","created_at":"2026-06-02T01:04:16.502244+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.04902","created_at":"2026-06-02T01:04:16.502244+00:00"},{"alias_kind":"pith_short_12","alias_value":"4YZWJ3NDD4KW","created_at":"2026-06-02T01:04:16.502244+00:00"},{"alias_kind":"pith_short_16","alias_value":"4YZWJ3NDD4KW5DD7","created_at":"2026-06-02T01:04:16.502244+00:00"},{"alias_kind":"pith_short_8","alias_value":"4YZWJ3ND","created_at":"2026-06-02T01:04:16.502244+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/4YZWJ3NDD4KW5DD7IFVMSXB6CA","json":"https://pith.science/pith/4YZWJ3NDD4KW5DD7IFVMSXB6CA.json","graph_json":"https://pith.science/api/pith-number/4YZWJ3NDD4KW5DD7IFVMSXB6CA/graph.json","events_json":"https://pith.science/api/pith-number/4YZWJ3NDD4KW5DD7IFVMSXB6CA/events.json","paper":"https://pith.science/paper/4YZWJ3ND"},"agent_actions":{"view_html":"https://pith.science/pith/4YZWJ3NDD4KW5DD7IFVMSXB6CA","download_json":"https://pith.science/pith/4YZWJ3NDD4KW5DD7IFVMSXB6CA.json","view_paper":"https://pith.science/paper/4YZWJ3ND","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.04902&json=true","fetch_graph":"https://pith.science/api/pith-number/4YZWJ3NDD4KW5DD7IFVMSXB6CA/graph.json","fetch_events":"https://pith.science/api/pith-number/4YZWJ3NDD4KW5DD7IFVMSXB6CA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4YZWJ3NDD4KW5DD7IFVMSXB6CA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4YZWJ3NDD4KW5DD7IFVMSXB6CA/action/storage_attestation","attest_author":"https://pith.science/pith/4YZWJ3NDD4KW5DD7IFVMSXB6CA/action/author_attestation","sign_citation":"https://pith.science/pith/4YZWJ3NDD4KW5DD7IFVMSXB6CA/action/citation_signature","submit_replication":"https://pith.science/pith/4YZWJ3NDD4KW5DD7IFVMSXB6CA/action/replication_record"}},"created_at":"2026-06-02T01:04:16.502244+00:00","updated_at":"2026-06-02T01:04:16.502244+00:00"}