{"id":"W4414170779","doi":"10.1109/iwqos65803.2025.11143333","title":"ConfAgent: Towards Intelligent Network Configuration Via LLM Agent","year":2025,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Shandong Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Adaptability; Context (archaeology); Key (lock); Routing (electronic design automation); Benchmark (surveying); Scale (ratio); Code (set theory); Configuration design","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001345521,0.0009818929,0.0004570601,0.0007623306,0.0004609263,0.00137481,0.002035158,0.0009734997,0.003045933],"category_scores_gemma":[0.004442454,0.0004822325,0.0006578405,0.0004082654,0.001028501,0.002069337,0.002314279,0.001339948,0.001242862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007552185,"about_ca_system_score_gemma":0.001068543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001798146,"about_ca_topic_score_gemma":0.003422374,"domain_scores_codex":[0.998976,0.0003542025,0.00006541491,0.0001896019,0.0003483587,0.00006656727],"domain_scores_gemma":[0.9987684,0.0004815057,0.0001242101,0.0003548951,0.000198356,0.0000726613],"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":[0.0003722197,0.0002838065,0.003187835,0.0004148012,0.00009178828,0.0007153519,0.0005845432,0.4626687,0.02881175,0.04592972,0.02997396,0.4269654],"study_design_scores_gemma":[0.00002678618,0.00004073179,0.0001033692,0.00001500733,0.00001019687,0.00007673824,0.00004228462,0.9747949,0.006217387,0.009849806,0.008809472,0.00001341251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01400514,0.0002071786,0.9581957,0.0002851546,0.0000728719,0.0001492734,0.0001647075,0.02208002,0.004839794],"genre_scores_gemma":[0.2379069,0.0001879395,0.7548209,0.000281085,0.00003159846,0.0002727389,0.0009936243,0.001515969,0.003989315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003045933,"threshold_uncertainty_score":0.01018971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618187331310761,"score_gpt":0.2547320063400373,"score_spread":0.2385501330269297,"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."}}