{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004017105,0.0001606495,0.000223663,0.0002389702,0.0002015785,0.0002006087,0.0008953353,0.00008983014,0.0000023826],"category_scores_gemma":[0.00006297934,0.0001668368,0.00006943418,0.0006886027,0.00001041711,0.0003078281,0.0008314368,0.0002236461,0.00001569065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001349815,"about_ca_system_score_gemma":0.0001439069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001486694,"about_ca_topic_score_gemma":0.00002071311,"domain_scores_codex":[0.9983267,0.00006391558,0.000242019,0.0004933181,0.0003182355,0.0005558345],"domain_scores_gemma":[0.9989414,0.00008256672,0.00003415914,0.0007474308,0.00006746757,0.0001269955],"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":[0.000001722813,0.00007106974,0.0002005702,0.00003356776,0.00001120301,0.000004483254,0.004482839,0.8595146,0.0004601752,0.13383,0.00004824097,0.001341586],"study_design_scores_gemma":[0.0002548705,0.000008747274,0.00007312201,0.00004119071,0.000008516445,0.000004601537,0.0002772455,0.9974852,0.0003130436,0.001339242,0.00002635299,0.0001678182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03584684,0.00007656495,0.9589563,0.0002616256,0.0002859805,0.0002543255,0.000003621869,0.0001582696,0.004156469],"genre_scores_gemma":[0.5405239,0.000001027518,0.4587778,0.0002577176,0.00003597374,0.000003920452,0.00000210544,0.000008426434,0.0003890458],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5046771,"threshold_uncertainty_score":0.6803409,"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."}}