{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CDYRNMKSIKXQGCCI4H25XV7GGL","short_pith_number":"pith:CDYRNMKS","schema_version":"1.0","canonical_sha256":"10f116b15242af030848e1f5dbd7e632c0c1839f268485cd27613c7cb7bac2ea","source":{"kind":"arxiv","id":"2509.03707","version":1},"attestation_state":"computed","paper":{"title":"Online Learning of Optimal Sequential Testing Policies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Qiyuan Chen, Raed Al Kontar","submitted_at":"2025-09-03T20:44:32Z","abstract_excerpt":"This paper studies an online learning problem that seeks optimal testing policies for a stream of subjects, each of whom can be evaluated through a sequence of candidate tests drawn from a common pool. We refer to this problem as the Online Testing Problem (OTP). Although conducting every candidate test for a subject provides more information, it is often preferable to select only a subset when tests are correlated and costly, and make decisions with partial information. If the joint distribution of test outcomes were known, the problem could be cast as a Markov Decision Process (MDP) and solv"},"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":"2509.03707","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-03T20:44:32Z","cross_cats_sorted":[],"title_canon_sha256":"76dc590ed74e50bd9d725b8d3dfaae586d377afae1ce07f9173d4e14c2ea8d79","abstract_canon_sha256":"271b53e7d4d8c178acc6a12e34d9d2af50b73eae75894b58f0e2e7a6434971ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:04:44.593504Z","signature_b64":"7Zdn5QfW4fKOBvbfomxPSkrVqJAQ72kTR5cUc00fqAm4mNlN4AxZkpt6kxDvX/psgtCWko73y87wvDuJ5AcRBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10f116b15242af030848e1f5dbd7e632c0c1839f268485cd27613c7cb7bac2ea","last_reissued_at":"2026-07-05T12:04:44.593054Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:04:44.593054Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Learning of Optimal Sequential Testing Policies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Qiyuan Chen, Raed Al Kontar","submitted_at":"2025-09-03T20:44:32Z","abstract_excerpt":"This paper studies an online learning problem that seeks optimal testing policies for a stream of subjects, each of whom can be evaluated through a sequence of candidate tests drawn from a common pool. We refer to this problem as the Online Testing Problem (OTP). Although conducting every candidate test for a subject provides more information, it is often preferable to select only a subset when tests are correlated and costly, and make decisions with partial information. If the joint distribution of test outcomes were known, the problem could be cast as a Markov Decision Process (MDP) and solv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.03707","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/2509.03707/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":"2509.03707","created_at":"2026-07-05T12:04:44.593107+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.03707v1","created_at":"2026-07-05T12:04:44.593107+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.03707","created_at":"2026-07-05T12:04:44.593107+00:00"},{"alias_kind":"pith_short_12","alias_value":"CDYRNMKSIKXQ","created_at":"2026-07-05T12:04:44.593107+00:00"},{"alias_kind":"pith_short_16","alias_value":"CDYRNMKSIKXQGCCI","created_at":"2026-07-05T12:04:44.593107+00:00"},{"alias_kind":"pith_short_8","alias_value":"CDYRNMKS","created_at":"2026-07-05T12:04:44.593107+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/CDYRNMKSIKXQGCCI4H25XV7GGL","json":"https://pith.science/pith/CDYRNMKSIKXQGCCI4H25XV7GGL.json","graph_json":"https://pith.science/api/pith-number/CDYRNMKSIKXQGCCI4H25XV7GGL/graph.json","events_json":"https://pith.science/api/pith-number/CDYRNMKSIKXQGCCI4H25XV7GGL/events.json","paper":"https://pith.science/paper/CDYRNMKS"},"agent_actions":{"view_html":"https://pith.science/pith/CDYRNMKSIKXQGCCI4H25XV7GGL","download_json":"https://pith.science/pith/CDYRNMKSIKXQGCCI4H25XV7GGL.json","view_paper":"https://pith.science/paper/CDYRNMKS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.03707&json=true","fetch_graph":"https://pith.science/api/pith-number/CDYRNMKSIKXQGCCI4H25XV7GGL/graph.json","fetch_events":"https://pith.science/api/pith-number/CDYRNMKSIKXQGCCI4H25XV7GGL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CDYRNMKSIKXQGCCI4H25XV7GGL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CDYRNMKSIKXQGCCI4H25XV7GGL/action/storage_attestation","attest_author":"https://pith.science/pith/CDYRNMKSIKXQGCCI4H25XV7GGL/action/author_attestation","sign_citation":"https://pith.science/pith/CDYRNMKSIKXQGCCI4H25XV7GGL/action/citation_signature","submit_replication":"https://pith.science/pith/CDYRNMKSIKXQGCCI4H25XV7GGL/action/replication_record"}},"created_at":"2026-07-05T12:04:44.593107+00:00","updated_at":"2026-07-05T12:04:44.593107+00:00"}