{"id":"W4407910063","doi":"10.2139/ssrn.5153814","title":"Tdri: Two-Phase Dialogue Refinement and Co-Adaptation for Interactive Image Generation","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adaptation (eye); Phase (matter); Image (mathematics); Computer science; Human–computer interaction; Artificial intelligence; Psychology; Chemistry","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":[0.00184745,0.001742825,0.001437675,0.0008519746,0.0007096812,0.001470908,0.003595543,0.002415958,0.0144617],"category_scores_gemma":[0.005646613,0.0008342834,0.001076186,0.000779082,0.0007645586,0.001881253,0.00477799,0.00244777,0.004971042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004238169,"about_ca_system_score_gemma":0.0007375432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003440812,"about_ca_topic_score_gemma":0.003619647,"domain_scores_codex":[0.9984288,0.0005209431,0.0000668476,0.0004199869,0.0004191708,0.0001441918],"domain_scores_gemma":[0.9978912,0.001161471,0.0000642988,0.0004235885,0.0003285473,0.0001308156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001275355,0.0004661315,0.0004379325,0.0003859405,0.0001354945,0.0003790685,0.0009461997,0.05521228,0.09220362,0.008010304,0.01376191,0.8267859],"study_design_scores_gemma":[0.0001248078,0.0001611847,0.0002692815,0.00002222855,0.00003365671,0.000185635,0.0001022222,0.9479051,0.03842084,0.005406627,0.007318717,0.00004970322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004436807,0.0001208866,0.9861636,0.00004551842,0.00008508153,0.0001203653,0.00007463215,0.007590307,0.001362863],"genre_scores_gemma":[0.1281644,0.0001040309,0.8620226,0.0001457456,0.00007511025,0.0004751992,0.0005152845,0.002285616,0.006212122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0144617,"threshold_uncertainty_score":0.04837918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02233089557443285,"score_gpt":0.3583350478680937,"score_spread":0.3360041522936608,"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."}}