{"id":"W4412699824","doi":"10.3390/robotics14080102","title":"MLLM-Search: A Zero-Shot Approach to Finding People Using Multimodal Large Language Models","year":2025,"lang":"en","type":"article","venue":"Robotics","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences North; Baycrest Hospital; Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL","keywords":"Zero (linguistics); Shot (pellet); Computer science; Artificial intelligence; Linguistics; Natural language processing; Philosophy","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":[0.001081349,0.001648992,0.001482275,0.001023456,0.0006474424,0.001460685,0.003616049,0.002189874,0.005350834],"category_scores_gemma":[0.004059584,0.0008660817,0.001871786,0.0008236361,0.0008928963,0.002883424,0.003257784,0.001846066,0.001847473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012591,"about_ca_system_score_gemma":0.001515505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01221375,"about_ca_topic_score_gemma":0.01882544,"domain_scores_codex":[0.9990572,0.0003029412,0.00004548378,0.0003024762,0.0002001283,0.00009181057],"domain_scores_gemma":[0.9989437,0.0006442509,0.00006660163,0.0001311109,0.0001373644,0.00007697051],"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":[0.0007383412,0.0003785015,0.001736988,0.0008613097,0.0003319543,0.0007942797,0.001810561,0.3902487,0.02703063,0.02443348,0.01655803,0.5350772],"study_design_scores_gemma":[0.00003128023,0.0001122776,0.0001606998,0.00002522885,0.00003016636,0.0001164518,0.0001979673,0.9777839,0.003600757,0.01461803,0.003292609,0.00003073647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00757626,0.0003576211,0.98538,0.0001890929,0.00004379683,0.00009887384,0.0002601468,0.004657622,0.001436454],"genre_scores_gemma":[0.2795882,0.0005088594,0.7088145,0.0008370688,0.00007336504,0.0004940365,0.001904067,0.001086291,0.00669355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01221375,"threshold_uncertainty_score":0.02428532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0891568815851013,"score_gpt":0.3237694848338047,"score_spread":0.2346126032487034,"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."}}