{"id":"W4417142339","doi":"10.48550/arxiv.2512.05343","title":"SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling","year":2025,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Nvidia; National Science Foundation","keywords":"Generative grammar; Object (grammar); Interface (matter); Key (lock); Range (aeronautics); Geometric primitive; User interface; Geometric modeling; Fidelity","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","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0008169801,0.001396341,0.002084953,0.001369006,0.0006004912,0.0003055807,0.001345102,0.0008697204,0.000628652],"category_scores_gemma":[0.0005287541,0.001813635,0.001049239,0.001285262,0.00008790031,0.0003192096,0.0008911262,0.001785303,0.001048187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168633,"about_ca_system_score_gemma":0.0004427639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002410285,"about_ca_topic_score_gemma":0.0004121032,"domain_scores_codex":[0.9941427,0.0003241822,0.001017114,0.002919517,0.0002531934,0.001343364],"domain_scores_gemma":[0.9958259,0.0005426323,0.000300611,0.001881135,0.0007485906,0.0007011295],"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":[0.0002471213,0.0001291276,0.000285873,0.000176124,0.001667668,0.000132523,0.0005703117,0.992784,0.001686948,0.0004283857,0.0001621121,0.001729783],"study_design_scores_gemma":[0.002149986,0.0001095478,0.00001155859,0.0005247885,0.002283993,0.000002128523,0.0001601212,0.9920635,0.0003265852,0.0006629548,0.0001293112,0.001575523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08139215,0.000188645,0.9124542,0.0005018642,0.000853721,0.0008869037,0.000461379,0.0005083295,0.002752785],"genre_scores_gemma":[0.9820535,0.0003723532,0.003551211,0.0004008194,0.001131655,0.000008150268,0.00007194398,0.0001164241,0.01229392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.908903,"threshold_uncertainty_score":0.9998787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02568873452817434,"score_gpt":0.1659235039447409,"score_spread":0.1402347694165666,"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."}}