{"id":"W4416034039","doi":"10.18653/v1/2025.findings-emnlp.918","title":"MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices","year":2025,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Korea Institute of Science and Technology Information; Institute for Information and Communications Technology Promotion; Korea Institute of Science and Technology","keywords":"Artificial neural network; Feature (linguistics); Architecture; Systems architecture; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001164359,0.001350796,0.0006999316,0.0008563971,0.0003441556,0.001143619,0.001862673,0.001068302,0.003359387],"category_scores_gemma":[0.004445762,0.0005107317,0.0009365706,0.0008611374,0.0004458079,0.001785681,0.001408377,0.001349264,0.0005470964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079278,"about_ca_system_score_gemma":0.001339915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01181544,"about_ca_topic_score_gemma":0.02522547,"domain_scores_codex":[0.999586,0.0001077864,0.00003467877,0.0001211926,0.0001106243,0.00003968089],"domain_scores_gemma":[0.9991296,0.0004946226,0.00007866301,0.0001280419,0.0001281561,0.00004102179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001679078,0.000146407,0.002940988,0.0002872841,0.0001393276,0.0001556734,0.0000996724,0.8306241,0.00491763,0.008636324,0.01211597,0.1397688],"study_design_scores_gemma":[0.000008138918,0.00001132801,0.00008824519,0.000002853633,0.000003325819,0.000008780225,0.0000087952,0.9962683,0.0004093989,0.002724247,0.0004633678,0.000003194842],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04805405,0.001004241,0.9269239,0.0006725489,0.0001106265,0.0001473113,0.001715919,0.01827797,0.003093294],"genre_scores_gemma":[0.5062253,0.0003550962,0.4853077,0.0004330066,0.00005106838,0.0002765747,0.003661054,0.0007225761,0.002967618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01181544,"threshold_uncertainty_score":0.02349329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01136126513391099,"score_gpt":0.2455111025200885,"score_spread":0.2341498373861775,"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."}}