{"id":"W7084087828","doi":"10.1109/newcas64648.2025.11107041","title":"Foggy- and Proximity-Aware Reinforcement Learning for Analog and Mixed-Signal Circuit Placement","year":2025,"lang":"en","type":"article","venue":"","topic":"Latin American socio-political dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Newfoundland and Labrador; Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Ocean Frontier Institute; Canada Foundation for Innovation","keywords":"Reinforcement learning; Process (computing); Automation; State (computer science); Electronic design automation; Circuit design; Integrated circuit layout; Integrated circuit","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":[0.0007681506,0.0007034128,0.0008405352,0.0004802578,0.0003972247,0.0005975973,0.0009971742,0.0007607783,0.001660557],"category_scores_gemma":[0.002526786,0.0003410226,0.0004451384,0.0003409723,0.0007905837,0.0006804801,0.0008743856,0.000936505,0.0002034095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019376,"about_ca_system_score_gemma":0.001090263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004347671,"about_ca_topic_score_gemma":0.005440924,"domain_scores_codex":[0.9996822,0.00009454298,0.00001279632,0.00007783542,0.00007854732,0.00005420574],"domain_scores_gemma":[0.9989758,0.0006353343,0.0001336902,0.00006942639,0.0001240131,0.00006174592],"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":[0.00004051546,0.00004605149,0.0006361033,0.00003440165,0.00002134972,0.00004050109,0.00003635244,0.9580828,0.001685175,0.004009028,0.0004354049,0.03493245],"study_design_scores_gemma":[0.000003695674,0.0000151316,0.00005613002,0.00000184339,0.000002871403,0.000005486593,0.000002647466,0.9980537,0.0002794115,0.001456652,0.0001205966,0.00000198135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0187196,0.0001520778,0.9790955,0.0001105622,0.00003490296,0.00002832483,0.00001608071,0.0003072259,0.001535734],"genre_scores_gemma":[0.901835,0.0001041409,0.09614983,0.0001101946,0.00002747895,0.00007069825,0.00003370036,0.00004363705,0.001625407],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004347671,"threshold_uncertainty_score":0.00864476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01763654965088169,"score_gpt":0.306209565709712,"score_spread":0.2885730160588303,"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."}}