{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YXZG3BULFDQ5OPRB6Z7YAB2X72","short_pith_number":"pith:YXZG3BUL","schema_version":"1.0","canonical_sha256":"c5f26d868b28e1d73e21f67f800757feaca5ac4b2e228e596ed1d6a0142b7bcb","source":{"kind":"arxiv","id":"2403.02054","version":1},"attestation_state":"computed","paper":{"title":"Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ameya D. Jagtap, Aniruddha Panda, Ankush Sharma, Arun Kumar Sagotra, Harshil Patel, Kaushic Kalyanaraman, Kaushik Koneripalli, Shuvayan Brahmachary, Subodh M. Joshi","submitted_at":"2024-03-04T13:57:37Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, prompting interest in their application as black-box optimizers. This paper asserts that LLMs possess the capability for zero-shot optimization across diverse scenarios, including multi-objective and high-dimensional problems. We introduce a novel population-based method for numerical optimization using LLMs called Language-Model-Based Evolutionary Optimizer (LEO). Our hypothesis is supported through numerical examples, spanning benchmark and industrial engineering problems such as supersonic nozzle shape optimizati"},"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":"2403.02054","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-04T13:57:37Z","cross_cats_sorted":[],"title_canon_sha256":"d2411d77c232053ae23f1d6b034cf73832420564c44f54f9be38cb45f7bafc45","abstract_canon_sha256":"9da22901316b461141c4585c521406b3976f0cf1e387eaa9d227b1d8ed4daa16"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:51:55.905587Z","signature_b64":"wraYRAzrtiz2CGunvADXmBA/y7UoVfNoyX3MgE6KABBOfngZ+L/yUV/odpA2pZ1SyoCDGaBSZtH67pn9QzgzCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5f26d868b28e1d73e21f67f800757feaca5ac4b2e228e596ed1d6a0142b7bcb","last_reissued_at":"2026-07-05T07:51:55.905212Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:51:55.905212Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ameya D. Jagtap, Aniruddha Panda, Ankush Sharma, Arun Kumar Sagotra, Harshil Patel, Kaushic Kalyanaraman, Kaushik Koneripalli, Shuvayan Brahmachary, Subodh M. Joshi","submitted_at":"2024-03-04T13:57:37Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, prompting interest in their application as black-box optimizers. This paper asserts that LLMs possess the capability for zero-shot optimization across diverse scenarios, including multi-objective and high-dimensional problems. We introduce a novel population-based method for numerical optimization using LLMs called Language-Model-Based Evolutionary Optimizer (LEO). Our hypothesis is supported through numerical examples, spanning benchmark and industrial engineering problems such as supersonic nozzle shape optimizati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.02054","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/2403.02054/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":"2403.02054","created_at":"2026-07-05T07:51:55.905270+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.02054v1","created_at":"2026-07-05T07:51:55.905270+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.02054","created_at":"2026-07-05T07:51:55.905270+00:00"},{"alias_kind":"pith_short_12","alias_value":"YXZG3BULFDQ5","created_at":"2026-07-05T07:51:55.905270+00:00"},{"alias_kind":"pith_short_16","alias_value":"YXZG3BULFDQ5OPRB","created_at":"2026-07-05T07:51:55.905270+00:00"},{"alias_kind":"pith_short_8","alias_value":"YXZG3BUL","created_at":"2026-07-05T07:51:55.905270+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/YXZG3BULFDQ5OPRB6Z7YAB2X72","json":"https://pith.science/pith/YXZG3BULFDQ5OPRB6Z7YAB2X72.json","graph_json":"https://pith.science/api/pith-number/YXZG3BULFDQ5OPRB6Z7YAB2X72/graph.json","events_json":"https://pith.science/api/pith-number/YXZG3BULFDQ5OPRB6Z7YAB2X72/events.json","paper":"https://pith.science/paper/YXZG3BUL"},"agent_actions":{"view_html":"https://pith.science/pith/YXZG3BULFDQ5OPRB6Z7YAB2X72","download_json":"https://pith.science/pith/YXZG3BULFDQ5OPRB6Z7YAB2X72.json","view_paper":"https://pith.science/paper/YXZG3BUL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.02054&json=true","fetch_graph":"https://pith.science/api/pith-number/YXZG3BULFDQ5OPRB6Z7YAB2X72/graph.json","fetch_events":"https://pith.science/api/pith-number/YXZG3BULFDQ5OPRB6Z7YAB2X72/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YXZG3BULFDQ5OPRB6Z7YAB2X72/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YXZG3BULFDQ5OPRB6Z7YAB2X72/action/storage_attestation","attest_author":"https://pith.science/pith/YXZG3BULFDQ5OPRB6Z7YAB2X72/action/author_attestation","sign_citation":"https://pith.science/pith/YXZG3BULFDQ5OPRB6Z7YAB2X72/action/citation_signature","submit_replication":"https://pith.science/pith/YXZG3BULFDQ5OPRB6Z7YAB2X72/action/replication_record"}},"created_at":"2026-07-05T07:51:55.905270+00:00","updated_at":"2026-07-05T07:51:55.905270+00:00"}