{"id":"W4391836026","doi":"10.48550/arxiv.2402.06957","title":"Architectural Neural Backdoors from First Principles","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005543071,0.002061776,0.001151901,0.001572413,0.001520622,0.00583623,0.003665216,0.003073305,0.005623172],"category_scores_gemma":[0.02889988,0.002270915,0.002025424,0.0006083976,0.009909159,0.01288627,0.007758374,0.007770699,0.001123572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002882742,"about_ca_system_score_gemma":0.00194903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00278292,"about_ca_topic_score_gemma":0.004383285,"domain_scores_codex":[0.994472,0.001571175,0.0003631703,0.001246026,0.001735193,0.0006124941],"domain_scores_gemma":[0.9843392,0.005761939,0.001149072,0.007478465,0.000984224,0.0002870457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002947108,0.0001044745,0.004927804,0.0006519155,0.0003106161,0.0005717567,0.001260366,0.1946195,0.01449941,0.6307466,0.005160297,0.1468527],"study_design_scores_gemma":[0.00003068618,0.0001306855,0.0005563648,0.0001978718,0.00007778453,0.0004459585,0.000168849,0.3350499,0.01613791,0.635461,0.01167811,0.00006480128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03078509,0.000691802,0.9559711,0.001747365,0.0001060533,0.0001018419,0.0001175376,0.002881751,0.007597527],"genre_scores_gemma":[0.706787,0.00103315,0.2833856,0.001086355,0.00008080516,0.0003476923,0.0003117901,0.001238443,0.005729245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00583623,"threshold_uncertainty_score":0.02931488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07969150709225993,"score_gpt":0.1843232594041434,"score_spread":0.1046317523118835,"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."}}