{"id":"W4415281380","doi":"10.1145/3768725.3768728","title":"MAGNET: Memory Tagging with Efficient Tag Prediction","year":2025,"lang":"","type":"article","venue":"","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"Identification (biology); Noise (video); Pattern recognition (psychology); Key (lock)","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":[0.001402241,0.001837944,0.001635583,0.001744313,0.001258729,0.00249106,0.003716115,0.002016695,0.009330504],"category_scores_gemma":[0.005784043,0.001148986,0.001228844,0.002134624,0.0008445044,0.004599838,0.004157866,0.001878694,0.01114015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007938361,"about_ca_system_score_gemma":0.001951964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002722976,"about_ca_topic_score_gemma":0.005742831,"domain_scores_codex":[0.9984459,0.0002908106,0.00009301986,0.0004633954,0.0004444307,0.0002623644],"domain_scores_gemma":[0.9958686,0.001176363,0.0002748273,0.001974866,0.0005255754,0.0001798181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002646196,0.0003998658,0.003067109,0.0004341564,0.0002263125,0.0004181348,0.0002304039,0.01729575,0.04450195,0.03301704,0.1507819,0.7469812],"study_design_scores_gemma":[0.0003343907,0.0005349804,0.001276474,0.0001223162,0.0002743505,0.0006845901,0.0001917618,0.6117625,0.1416312,0.1671165,0.07575937,0.0003116171],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01877955,0.001428141,0.8805645,0.0005806198,0.001140699,0.0001389597,0.002439807,0.08974663,0.00518107],"genre_scores_gemma":[0.3115755,0.0005643752,0.6448079,0.001354872,0.0005978274,0.0003132507,0.007621514,0.006335753,0.026829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009330504,"threshold_uncertainty_score":0.03121364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009152397977757598,"score_gpt":0.2289413252663679,"score_spread":0.2197889272886103,"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."}}