{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:R4T4DNEE6YOHAM32N3MLY6HE7D","short_pith_number":"pith:R4T4DNEE","schema_version":"1.0","canonical_sha256":"8f27c1b484f61c70337a6ed8bc78e4f8d72c8ccd04d9bcee63b5924e19c53ca9","source":{"kind":"arxiv","id":"2606.09002","version":1},"attestation_state":"computed","paper":{"title":"Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Deqi Zheng, Xiaoyang Xu, Yuhong Yang","submitted_at":"2026-06-08T03:58:38Z","abstract_excerpt":"We study a stochastic multi-armed bandit problem in which the set of available arms expands over time. This setting arises in sequential experimentation when new actions or treatments become available during an ongoing study, making regret against a single best arm in hindsight inappropriate. We instead evaluate performance relative to the best arm currently available, leading to a dynamic-regret criterion for arriving-arm environments. To address the resulting challenges of arrival information discrepancy (AID) and a drifting benchmark (DB), we propose UCB for Arriving Arms (UCB-AA), an elimi"},"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":"2606.09002","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-06-08T03:58:38Z","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"title_canon_sha256":"c207ee5831a5054e3e9774ee01dff9f56ceb6c16640cf04fbc26b57576f920dc","abstract_canon_sha256":"98ad6dbc559071f55ca98ea4b1f14fc3c050ec7696b9355ebd633f1c8b672e90"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:52.869833Z","signature_b64":"mFzvc/vfn0PMwFm7QIvUN5xIiYxd4deQ6TJiT/007trtNbpt4s54DTo0M8KzQf+gtC7cVfdqMLOCTjNYCYyGDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f27c1b484f61c70337a6ed8bc78e4f8d72c8ccd04d9bcee63b5924e19c53ca9","last_reissued_at":"2026-06-09T02:07:52.868877Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:52.868877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Deqi Zheng, Xiaoyang Xu, Yuhong Yang","submitted_at":"2026-06-08T03:58:38Z","abstract_excerpt":"We study a stochastic multi-armed bandit problem in which the set of available arms expands over time. This setting arises in sequential experimentation when new actions or treatments become available during an ongoing study, making regret against a single best arm in hindsight inappropriate. We instead evaluate performance relative to the best arm currently available, leading to a dynamic-regret criterion for arriving-arm environments. To address the resulting challenges of arrival information discrepancy (AID) and a drifting benchmark (DB), we propose UCB for Arriving Arms (UCB-AA), an elimi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.09002","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/2606.09002/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":"2606.09002","created_at":"2026-06-09T02:07:52.869043+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.09002v1","created_at":"2026-06-09T02:07:52.869043+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.09002","created_at":"2026-06-09T02:07:52.869043+00:00"},{"alias_kind":"pith_short_12","alias_value":"R4T4DNEE6YOH","created_at":"2026-06-09T02:07:52.869043+00:00"},{"alias_kind":"pith_short_16","alias_value":"R4T4DNEE6YOHAM32","created_at":"2026-06-09T02:07:52.869043+00:00"},{"alias_kind":"pith_short_8","alias_value":"R4T4DNEE","created_at":"2026-06-09T02:07:52.869043+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/R4T4DNEE6YOHAM32N3MLY6HE7D","json":"https://pith.science/pith/R4T4DNEE6YOHAM32N3MLY6HE7D.json","graph_json":"https://pith.science/api/pith-number/R4T4DNEE6YOHAM32N3MLY6HE7D/graph.json","events_json":"https://pith.science/api/pith-number/R4T4DNEE6YOHAM32N3MLY6HE7D/events.json","paper":"https://pith.science/paper/R4T4DNEE"},"agent_actions":{"view_html":"https://pith.science/pith/R4T4DNEE6YOHAM32N3MLY6HE7D","download_json":"https://pith.science/pith/R4T4DNEE6YOHAM32N3MLY6HE7D.json","view_paper":"https://pith.science/paper/R4T4DNEE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.09002&json=true","fetch_graph":"https://pith.science/api/pith-number/R4T4DNEE6YOHAM32N3MLY6HE7D/graph.json","fetch_events":"https://pith.science/api/pith-number/R4T4DNEE6YOHAM32N3MLY6HE7D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R4T4DNEE6YOHAM32N3MLY6HE7D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R4T4DNEE6YOHAM32N3MLY6HE7D/action/storage_attestation","attest_author":"https://pith.science/pith/R4T4DNEE6YOHAM32N3MLY6HE7D/action/author_attestation","sign_citation":"https://pith.science/pith/R4T4DNEE6YOHAM32N3MLY6HE7D/action/citation_signature","submit_replication":"https://pith.science/pith/R4T4DNEE6YOHAM32N3MLY6HE7D/action/replication_record"}},"created_at":"2026-06-09T02:07:52.869043+00:00","updated_at":"2026-06-09T02:07:52.869043+00:00"}