{"id":"W4389544300","doi":"10.1109/acsos-c58168.2023.00048","title":"Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Adaptation (eye); Conversation; Multi-agent system; Key (lock); Adaptability; Multitude; Distributed computing; Human–computer interaction; Knowledge management; Artificial intelligence; Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00176647,0.0004913533,0.0006620198,0.0004510526,0.0008555366,0.002246021,0.001804939,0.001190282,0.001758728],"category_scores_gemma":[0.004659486,0.0003781468,0.0008211265,0.0004079177,0.001628068,0.003130498,0.003047354,0.001355862,0.0005830437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099252,"about_ca_system_score_gemma":0.001207315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002403705,"about_ca_topic_score_gemma":0.002237776,"domain_scores_codex":[0.9987451,0.0005708106,0.00008869037,0.000234241,0.0002569998,0.0001040523],"domain_scores_gemma":[0.9983006,0.0008549077,0.0002147517,0.0003132701,0.0001957708,0.0001206765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001536538,0.0001562683,0.001776457,0.000238292,0.0001651143,0.0008722098,0.001593052,0.5138785,0.01268694,0.4052946,0.003279188,0.05990568],"study_design_scores_gemma":[0.00002506358,0.00003926559,0.000111371,0.00001398952,0.00001690155,0.00009612252,0.00007619618,0.9105265,0.001012756,0.08360248,0.004460097,0.00001922036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02776242,0.0002893867,0.9606827,0.0009545755,0.00008745395,0.0001289575,0.00007748139,0.001119451,0.008897529],"genre_scores_gemma":[0.7413908,0.0003422256,0.2521939,0.0003584803,0.00008635755,0.0004324269,0.0001911817,0.000134831,0.004869751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002403705,"threshold_uncertainty_score":0.009342134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0295361948046554,"score_gpt":0.2657220757973752,"score_spread":0.2361858809927198,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}