{"id":"W4400612051","doi":"10.48550/arxiv.2407.08663","title":"Mon CHÉRI: Mitigating Uninitialized Memory Access with Conditional Capabilities","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; University of Waterloo; KU Leuven; Waalse Gewest","keywords":"Computer science; Computer hardware; Embedded system; Parallel computing","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"],"consensus_categories":[],"category_scores_codex":[0.0002484322,0.0003541163,0.0003502707,0.0003628167,0.0002224436,0.0005264716,0.00187781,0.0002319221,0.0000257486],"category_scores_gemma":[0.00003105803,0.000366789,0.0001563795,0.0006157199,0.0002451556,0.0004160247,0.002854164,0.0007099881,0.00003063161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001872778,"about_ca_system_score_gemma":0.0004483095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003232606,"about_ca_topic_score_gemma":0.00001818383,"domain_scores_codex":[0.997984,0.0001844444,0.0002399527,0.001116643,0.000157075,0.0003178997],"domain_scores_gemma":[0.9983662,0.0001433181,0.0002366609,0.000842403,0.000275783,0.0001356537],"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":[0.00001980168,0.00004492419,0.0002003172,0.0002525349,0.0001174255,0.0003287838,0.0004310122,0.7316233,0.000006716267,0.2657251,0.001035438,0.0002146781],"study_design_scores_gemma":[0.0002962229,0.00004830839,0.0001253719,0.0003105202,0.00004927466,0.00001426495,0.0000627692,0.6954483,0.0005184511,0.3025227,0.0001414019,0.0004623826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09441181,0.00009630524,0.8925462,0.0003016424,0.0003598,0.0003246928,0.00003650521,0.001723947,0.01019915],"genre_scores_gemma":[0.9838663,0.00004935433,0.01370267,0.0001644631,0.00009847836,0.000004606577,0.00007134635,0.00002727526,0.002015541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8894545,"threshold_uncertainty_score":0.9998784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06654008795038215,"score_gpt":0.2227902766612339,"score_spread":0.1562501887108517,"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."}}