{"id":"W4416893698","doi":"10.1016/j.engappai.2025.113378","title":"A reward-directed diffusion framework for generative design","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Compute Canada","keywords":"Diffusion; Generative grammar; Generative model; Generative Design; Surface fitting","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":[0.002148535,0.0009403133,0.001284378,0.001099632,0.0008415663,0.001763863,0.002282753,0.002265269,0.006055799],"category_scores_gemma":[0.005443636,0.0009149296,0.001258903,0.00122055,0.001850964,0.001635272,0.002136145,0.001876166,0.001017281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001557192,"about_ca_system_score_gemma":0.001560093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004726558,"about_ca_topic_score_gemma":0.005457934,"domain_scores_codex":[0.9991412,0.0004224846,0.0000356023,0.000114458,0.0002252409,0.00006103009],"domain_scores_gemma":[0.9982285,0.001209927,0.00009311755,0.0001560328,0.0002204188,0.00009203427],"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":[0.00001924202,0.00002766574,0.0001532201,0.00006684917,0.00002399757,0.00005157641,0.00008929119,0.5912002,0.001128387,0.3851511,0.0009716786,0.02111662],"study_design_scores_gemma":[0.000008971871,0.00001097319,0.00002009956,0.000009367669,0.000006318071,0.00001407622,0.000004736758,0.921367,0.0001776411,0.07663876,0.001735339,0.000006827881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001278782,0.0001451929,0.9957983,0.0001209756,0.00001597453,0.00001949554,0.00001827351,0.00007126251,0.00253166],"genre_scores_gemma":[0.3001311,0.0009868791,0.6805926,0.0001920238,0.0001022144,0.0003794367,0.0001367327,0.0003312942,0.01714774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006055799,"threshold_uncertainty_score":0.02025867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02645839830539809,"score_gpt":0.3086985925554244,"score_spread":0.2822401942500263,"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."}}