{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:U42RL2IA6A52C2F27GXHF2425S","short_pith_number":"pith:U42RL2IA","schema_version":"1.0","canonical_sha256":"a73515e900f03ba168baf9ae72eb9aec9b14167221c777c432b2884beef9f2ed","source":{"kind":"arxiv","id":"2603.16474","version":2},"attestation_state":"computed","paper":{"title":"Practical MCTS-based Query Optimization: A Reproducibility Study and new MCTS algorithm for complex queries","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Alena Rybakina, Alexander Demin, Denis Ponomaryov, Konstantin Gilev, Sergey Kudashev, Vladimir Burlakov, Yuriy Dorn","submitted_at":"2026-03-17T13:01:29Z","abstract_excerpt":"Monte Carlo Tree Search (MCTS) has been proposed as a transformative approach to join-order optimization in database query processing, with recent frameworks such as AlphaJoin and HyperQO claiming to outperform traditional methods. However, the fact that these frameworks rely on learned cost models raises concerns related to generalizability and deployment readiness. This paper presents a comprehensive reproducibility study of these methods, revealing that they often fail to support the claimed performance gains when subjected to diverse workloads.\n  Through an ablation study, we diagnose the "},"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":"2603.16474","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2026-03-17T13:01:29Z","cross_cats_sorted":[],"title_canon_sha256":"e8aae14e7f321276f9b4280bc6c922db87ac4fce35c0e37005eae992cde4d6f3","abstract_canon_sha256":"1d31ac1e84ed00c90f5444bbe6beb79c651aa6717f971b641408520a92ddaf27"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:22.566244Z","signature_b64":"PeKo0NKVgGFyZX/QWSSOPD6WcKwFBoTwatio/bBd7QQ6MXX1omUEZ9yaVoOgecPBtXtb9rtIH/Zfjnr0L0MqCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a73515e900f03ba168baf9ae72eb9aec9b14167221c777c432b2884beef9f2ed","last_reissued_at":"2026-06-23T02:13:22.565739Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:22.565739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Practical MCTS-based Query Optimization: A Reproducibility Study and new MCTS algorithm for complex queries","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Alena Rybakina, Alexander Demin, Denis Ponomaryov, Konstantin Gilev, Sergey Kudashev, Vladimir Burlakov, Yuriy Dorn","submitted_at":"2026-03-17T13:01:29Z","abstract_excerpt":"Monte Carlo Tree Search (MCTS) has been proposed as a transformative approach to join-order optimization in database query processing, with recent frameworks such as AlphaJoin and HyperQO claiming to outperform traditional methods. However, the fact that these frameworks rely on learned cost models raises concerns related to generalizability and deployment readiness. This paper presents a comprehensive reproducibility study of these methods, revealing that they often fail to support the claimed performance gains when subjected to diverse workloads.\n  Through an ablation study, we diagnose the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.16474","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/2603.16474/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":"2603.16474","created_at":"2026-06-23T02:13:22.565799+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.16474v2","created_at":"2026-06-23T02:13:22.565799+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.16474","created_at":"2026-06-23T02:13:22.565799+00:00"},{"alias_kind":"pith_short_12","alias_value":"U42RL2IA6A52","created_at":"2026-06-23T02:13:22.565799+00:00"},{"alias_kind":"pith_short_16","alias_value":"U42RL2IA6A52C2F2","created_at":"2026-06-23T02:13:22.565799+00:00"},{"alias_kind":"pith_short_8","alias_value":"U42RL2IA","created_at":"2026-06-23T02:13:22.565799+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/U42RL2IA6A52C2F27GXHF2425S","json":"https://pith.science/pith/U42RL2IA6A52C2F27GXHF2425S.json","graph_json":"https://pith.science/api/pith-number/U42RL2IA6A52C2F27GXHF2425S/graph.json","events_json":"https://pith.science/api/pith-number/U42RL2IA6A52C2F27GXHF2425S/events.json","paper":"https://pith.science/paper/U42RL2IA"},"agent_actions":{"view_html":"https://pith.science/pith/U42RL2IA6A52C2F27GXHF2425S","download_json":"https://pith.science/pith/U42RL2IA6A52C2F27GXHF2425S.json","view_paper":"https://pith.science/paper/U42RL2IA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.16474&json=true","fetch_graph":"https://pith.science/api/pith-number/U42RL2IA6A52C2F27GXHF2425S/graph.json","fetch_events":"https://pith.science/api/pith-number/U42RL2IA6A52C2F27GXHF2425S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U42RL2IA6A52C2F27GXHF2425S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U42RL2IA6A52C2F27GXHF2425S/action/storage_attestation","attest_author":"https://pith.science/pith/U42RL2IA6A52C2F27GXHF2425S/action/author_attestation","sign_citation":"https://pith.science/pith/U42RL2IA6A52C2F27GXHF2425S/action/citation_signature","submit_replication":"https://pith.science/pith/U42RL2IA6A52C2F27GXHF2425S/action/replication_record"}},"created_at":"2026-06-23T02:13:22.565799+00:00","updated_at":"2026-06-23T02:13:22.565799+00:00"}