{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GOTGYCX7ZIPKBP2225JGWO2B2V","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0c3639ad3358c20e9baf9822579194b8c6a675067c18542d6751cf7e628b4b75","cross_cats_sorted":["cs.CL","cs.LG","cs.SE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-10-22T17:56:08Z","title_canon_sha256":"98904eb89e7e5d0d4aa80e2383f90cc6a56607fcdf04d7a392950e8efefbbb3c"},"schema_version":"1.0","source":{"id":"2410.17238","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.17238","created_at":"2026-07-05T09:24:13Z"},{"alias_kind":"arxiv_version","alias_value":"2410.17238v1","created_at":"2026-07-05T09:24:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.17238","created_at":"2026-07-05T09:24:13Z"},{"alias_kind":"pith_short_12","alias_value":"GOTGYCX7ZIPK","created_at":"2026-07-05T09:24:13Z"},{"alias_kind":"pith_short_16","alias_value":"GOTGYCX7ZIPKBP22","created_at":"2026-07-05T09:24:13Z"},{"alias_kind":"pith_short_8","alias_value":"GOTGYCX7","created_at":"2026-07-05T09:24:13Z"}],"graph_snapshots":[{"event_id":"sha256:30e03c72e39051a89d5cd6ab749188b8f2ffac9d4d7b34845f2f34e0de9a8af4","target":"graph","created_at":"2026-07-05T09:24:13Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2410.17238/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks that autonomously build pipelines. While LLM-based agents have shown promise in automating machine learning tasks, they often generate low-diversity and suboptimal code, even after multiple iterations. To overcome these limitations, we introduce Tree-Search Enhanced LLM Agents (SELA), an innovative agent-based system that leverages Monte Carlo Tree Search (MCTS) to optimize the AutoML process. By representing pipeli","authors_text":"Bangbang Liu, Bang Liu, Ceyao Zhang, Chenglin Wu, Duyi Pan, Guanghao Mei, Jacky Kwok, Sirui Hong, Tianqi Pang, Yaying Fei, Yizhang Lin, Yizhou Chi","cross_cats":["cs.CL","cs.LG","cs.SE"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-10-22T17:56:08Z","title":"SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.17238","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b5733a1174a30874508f480cd4b086388104192cc42d491067adf97ab0f4456a","target":"record","created_at":"2026-07-05T09:24:13Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0c3639ad3358c20e9baf9822579194b8c6a675067c18542d6751cf7e628b4b75","cross_cats_sorted":["cs.CL","cs.LG","cs.SE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-10-22T17:56:08Z","title_canon_sha256":"98904eb89e7e5d0d4aa80e2383f90cc6a56607fcdf04d7a392950e8efefbbb3c"},"schema_version":"1.0","source":{"id":"2410.17238","kind":"arxiv","version":1}},"canonical_sha256":"33a66c0affca1ea0bf5ad7526b3b41d551abb8af62f379acf9c10c67dd2bce82","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"33a66c0affca1ea0bf5ad7526b3b41d551abb8af62f379acf9c10c67dd2bce82","first_computed_at":"2026-07-05T09:24:13.983972Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:24:13.983972Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HqXqp8ZE0zbfm0TVWfN0qDjaPGFUnWACgTEuSotvsdQ9Pq3CdXzjLXoVYWVjAwpL2snLhGUGrdB6sVgiFD2XDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:24:13.984387Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.17238","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b5733a1174a30874508f480cd4b086388104192cc42d491067adf97ab0f4456a","sha256:30e03c72e39051a89d5cd6ab749188b8f2ffac9d4d7b34845f2f34e0de9a8af4"],"state_sha256":"52d0ec29a4367b94507589db9013a0bb9c8ce97a2374627f2d016d090ec3d38e"}