{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KZEKHK4AQBS6FOC3IRURPEKJTL","short_pith_number":"pith:KZEKHK4A","schema_version":"1.0","canonical_sha256":"5648a3ab808065e2b85b44691791499ad703a457dfc09f08b7e812a13945dbaa","source":{"kind":"arxiv","id":"2410.02958","version":2},"attestation_state":"computed","paper":{"title":"AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.MA"],"primary_cat":"cs.LG","authors_text":"Patara Trirat, Sung Ju Hwang, Wonyong Jeong","submitted_at":"2024-10-03T20:01:09Z","abstract_excerpt":"Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up complex tools, which is in general time-consuming and requires a large amount of human effort. Therefore, recent works have started exploiting large language models (LLM) to lessen such burden and increase the usability of AutoML frameworks via a natural language interface, allowing non-expert users to build their data-driven solutions. These methods, however, a"},"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":"2410.02958","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T20:01:09Z","cross_cats_sorted":["cs.AI","cs.CL","cs.MA"],"title_canon_sha256":"ac65e3efe385c75da395960c1f615a331bba9d30401ed151d361cdb3ff25490a","abstract_canon_sha256":"b39bb2265624305b17cdfa11cefd0ab2edaf01be03f90ce4c3084a802e80af87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:16:51.602484Z","signature_b64":"enBRPSa/MCZSYytSezkYuAXvUa8qGau8d/J/9BvSRoL7Otp/2eJFT6efOAnnhiTtYQx6JIzVnRpPKGaHrGJGAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5648a3ab808065e2b85b44691791499ad703a457dfc09f08b7e812a13945dbaa","last_reissued_at":"2026-07-05T11:16:51.601828Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:16:51.601828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.MA"],"primary_cat":"cs.LG","authors_text":"Patara Trirat, Sung Ju Hwang, Wonyong Jeong","submitted_at":"2024-10-03T20:01:09Z","abstract_excerpt":"Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up complex tools, which is in general time-consuming and requires a large amount of human effort. Therefore, recent works have started exploiting large language models (LLM) to lessen such burden and increase the usability of AutoML frameworks via a natural language interface, allowing non-expert users to build their data-driven solutions. These methods, however, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02958","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/2410.02958/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":"2410.02958","created_at":"2026-07-05T11:16:51.601907+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.02958v2","created_at":"2026-07-05T11:16:51.601907+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02958","created_at":"2026-07-05T11:16:51.601907+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZEKHK4AQBS6","created_at":"2026-07-05T11:16:51.601907+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZEKHK4AQBS6FOC3","created_at":"2026-07-05T11:16:51.601907+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZEKHK4A","created_at":"2026-07-05T11:16:51.601907+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":13,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20394","citing_title":"Agentic AutoResearch forSpace Autonomy: An Auditable, LLM-Driven Research Agent for Aerospace Control Problems","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2606.17915","citing_title":"Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12376","citing_title":"ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20728","citing_title":"VTOS: Learning to Orchestrate Vision Tools by Co-Searching Solutions and Observers","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2509.23986","citing_title":"TusoAI: Agentic Optimization for Scientific Methods","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2508.10177","citing_title":"KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2511.20857","citing_title":"Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory","ref_index":76,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03430","citing_title":"Scaling Multi-agent Systems: A Smart Middleware for Improving Agent Interactions","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12376","citing_title":"ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14655","citing_title":"AgentGA: Evolving Code Solutions in Agent-Seed Space","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18133","citing_title":"Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures","ref_index":85,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14655","citing_title":"AgentGA: Evolving Code Solutions in Agent-Seed Space","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20261","citing_title":"Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data","ref_index":84,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KZEKHK4AQBS6FOC3IRURPEKJTL","json":"https://pith.science/pith/KZEKHK4AQBS6FOC3IRURPEKJTL.json","graph_json":"https://pith.science/api/pith-number/KZEKHK4AQBS6FOC3IRURPEKJTL/graph.json","events_json":"https://pith.science/api/pith-number/KZEKHK4AQBS6FOC3IRURPEKJTL/events.json","paper":"https://pith.science/paper/KZEKHK4A"},"agent_actions":{"view_html":"https://pith.science/pith/KZEKHK4AQBS6FOC3IRURPEKJTL","download_json":"https://pith.science/pith/KZEKHK4AQBS6FOC3IRURPEKJTL.json","view_paper":"https://pith.science/paper/KZEKHK4A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.02958&json=true","fetch_graph":"https://pith.science/api/pith-number/KZEKHK4AQBS6FOC3IRURPEKJTL/graph.json","fetch_events":"https://pith.science/api/pith-number/KZEKHK4AQBS6FOC3IRURPEKJTL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZEKHK4AQBS6FOC3IRURPEKJTL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZEKHK4AQBS6FOC3IRURPEKJTL/action/storage_attestation","attest_author":"https://pith.science/pith/KZEKHK4AQBS6FOC3IRURPEKJTL/action/author_attestation","sign_citation":"https://pith.science/pith/KZEKHK4AQBS6FOC3IRURPEKJTL/action/citation_signature","submit_replication":"https://pith.science/pith/KZEKHK4AQBS6FOC3IRURPEKJTL/action/replication_record"}},"created_at":"2026-07-05T11:16:51.601907+00:00","updated_at":"2026-07-05T11:16:51.601907+00:00"}