{"id":"W4409364220","doi":"10.1609/aaai.v39i17.33957","title":"GenPlan: Generative Sequence Models as Adaptive Planners","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generative grammar; Sequence (biology); Computer science; Artificial intelligence; Biology","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":[0.001161335,0.001279618,0.0009543233,0.0007238649,0.0003856659,0.001100005,0.002309128,0.001451884,0.004048008],"category_scores_gemma":[0.004708308,0.001003124,0.001209379,0.0007717998,0.00146555,0.001536957,0.001848416,0.002557684,0.0009389937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383958,"about_ca_system_score_gemma":0.001917033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007724665,"about_ca_topic_score_gemma":0.01025884,"domain_scores_codex":[0.9994121,0.0002109986,0.00002536414,0.0001499011,0.0001486336,0.00005296091],"domain_scores_gemma":[0.9982328,0.001339994,0.0001064228,0.0001415482,0.00008809506,0.0000910923],"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":[0.00004264864,0.00001633838,0.0002412461,0.00004843096,0.00001789623,0.00004569522,0.00005026963,0.9518292,0.0006079017,0.02435092,0.001352906,0.02139652],"study_design_scores_gemma":[0.000008757323,0.00000737193,0.00001942517,0.000005242148,0.000002658396,0.000008639199,0.000002934276,0.9798656,0.0002313279,0.01895525,0.0008890316,0.000003624785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004047333,0.0002580328,0.9923475,0.000204507,0.00003475785,0.00004176697,0.0001870857,0.001443182,0.00143593],"genre_scores_gemma":[0.3966394,0.000940318,0.5922936,0.0004614102,0.0001156004,0.0007484914,0.001537082,0.001083831,0.006180142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007724665,"threshold_uncertainty_score":0.0153594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1034813939047148,"score_gpt":0.3105344415010804,"score_spread":0.2070530475963656,"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."}}