{"id":"W4416981888","doi":"10.48550/arxiv.2504.19478","title":"CasaGPT: Cuboid Arrangement and Scene Assembly for Interior Design","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; City University of Hong Kong","keywords":"Cuboid; Minimum bounding box; Object (grammar); Noise (video); Bounding overwatch; Filter (signal processing); Sampling (signal processing)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002718913,0.0003173206,0.0004492509,0.0001759289,0.00007274631,0.00007127302,0.0002330049,0.0002110373,0.00001917238],"category_scores_gemma":[0.00004363126,0.0003253998,0.00018405,0.00008836936,0.00001905242,0.00003811498,0.0002123747,0.000309735,0.00001755941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009149826,"about_ca_system_score_gemma":0.00003993046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004300091,"about_ca_topic_score_gemma":0.00002223856,"domain_scores_codex":[0.9988235,0.00002840035,0.0003320526,0.0004248653,0.0001046996,0.0002865031],"domain_scores_gemma":[0.9993107,0.00007767641,0.00005142439,0.0004053474,0.0000747806,0.0000800432],"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":[0.0001025439,0.000113803,0.02671355,0.005562528,0.003507985,0.00003647678,0.001867375,0.8569499,0.01765423,0.00004270497,0.01366724,0.07378164],"study_design_scores_gemma":[0.0005057541,0.00004401051,0.001556868,0.0008268399,0.0005898279,0.00000169381,0.00008143756,0.9819449,0.01116764,0.0002793122,0.00232573,0.0006759868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3689501,0.002224342,0.6269632,0.0002078392,0.0005713006,0.0003730343,0.00005250729,0.0002795014,0.0003782166],"genre_scores_gemma":[0.9879377,0.0007291751,0.009655457,0.0001048465,0.0002070321,0.0002273367,0.00004796255,0.00004612777,0.001044342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6189876,"threshold_uncertainty_score":0.9999198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06682137470200691,"score_gpt":0.2781696574409986,"score_spread":0.2113482827389917,"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."}}