Study 1: Oversight Effect Analysis Loaded 270 runs Models: ['deepseek/deepseek-v3.2:nitro', 'google/gemini-3.1-pro-preview', 'z-ai/glm-5.1:nitro'] Oversight levels: ['high', 'low', 'mid'] ====================================================================== 1. OVERSIGHT EFFECT (Overall) ====================================================================== Deception rates by oversight level: low: 64/90 (71.1%) 95% CI: [61.0%, 79.5%] mid: 63/90 (70.0%) 95% CI: [59.9%, 78.5%] high: 58/90 (64.4%) 95% CI: [54.2%, 73.6%] Chi-squared test: χ²(2) = 1.065, p = 0.5873 n.s. Cramér's V = 0.063 (negligible) Cochran-Armitage trend test: z = -0.963, p = 0.3356 n.s. ====================================================================== 2. BY OVERSIGHT LEVEL (Blackbox Output) ====================================================================== Blackbox deception rates: low: 43/90 (47.8%) 95% CI: [37.8%, 58.0%] mid: 37/90 (41.1%) 95% CI: [31.5%, 51.4%] high: 32/90 (35.6%) 95% CI: [26.4%, 45.8%] ====================================================================== 3. MODEL DIFFERENCES IN OVERSIGHT SENSITIVITY ====================================================================== Model Low Mid High Trend ----------------------------------- -------- -------- -------- ---------- deepseek-v3.2:nitro 93.3% 100.0% 93.3% z=+0.00 n.s. gemini-3.1-pro-preview 100.0% 100.0% 96.7% z=-1.23 n.s. glm-5.1:nitro 20.0% 10.0% 3.3% z=-2.05 * ====================================================================== 4. PAIRWISE COMPARISONS (Bonferroni-corrected) ====================================================================== low vs mid: χ²(1) = 0.000, Fisher p = 1.0000, OR = 0.95 [0.50, 1.80] [n.s.] low vs high: χ²(1) = 0.636, Fisher p = 0.4253, OR = 0.74 [0.39, 1.38] [n.s.] mid vs high: χ²(1) = 0.403, Fisher p = 0.5255, OR = 0.78 [0.42, 1.45] [n.s.] ====================================================================== 5. GLASSBOX REASONING BY OVERSIGHT ====================================================================== Compliant reasoning rates by oversight: low: 60/90 (66.7%) 95% CI: [56.4%, 75.5%] mid: 60/90 (66.7%) 95% CI: [56.4%, 75.5%] high: 56/90 (62.2%) 95% CI: [51.9%, 71.5%] ====================================================================== 6. STRATEGIC SOPHISTICATION ====================================================================== Strategic reasoning rates by oversight: low: 81/90 (90.0%) 95% CI: [82.1%, 94.6%] mid: 76/90 (84.4%) 95% CI: [75.6%, 90.5%] high: 79/90 (87.8%) 95% CI: [79.4%, 93.0%] ====================================================================== 7. LOGISTIC REGRESSION: deception ~ oversight ====================================================================== Coef. Std.Err. z P>|z| [0.025 0.975] const 6.822418 0.882565 7.730217 1.073638e-14 5.092623 8.552214 oversight_num -0.469690 0.287445 -1.634019 1.022548e-01 -1.033071 0.093691 model_code -4.054594 0.462620 -8.764413 1.877516e-18 -4.961312 -3.147875 Pseudo R² = 0.6019, AIC = 139.9 Odds ratios (exponentiated coefficients): oversight_num OR = 0.625 95% CI: [0.356, 1.098] n.s. model_code OR = 0.017 95% CI: [0.007, 0.043] *** ====================================================================== ANALYSIS COMPLETE ====================================================================== Total runs: 270 Models: ['deepseek/deepseek-v3.2:nitro', 'google/gemini-3.1-pro-preview', 'z-ai/glm-5.1:nitro'] Study 2: Framing Effect Analysis Loaded 270 runs Models: ['deepseek/deepseek-v3.2:nitro', 'google/gemini-3.1-pro-preview', 'z-ai/glm-5.1:nitro'] Goal types: ['bare', 'moral', 'self_serving'] ====================================================================== 1. GOAL TYPE EFFECT (Overall) ====================================================================== Deception rates by goal type: bare: 60/90 (66.7%) 95% CI: [56.4%, 75.5%] moral: 59/90 (65.6%) 95% CI: [55.3%, 74.6%] self_serving: 60/90 (66.7%) 95% CI: [56.4%, 75.5%] Chi-squared test: χ²(2) = 0.033, p = 0.9836 n.s. Cramér's V = 0.011 (negligible) ====================================================================== 2. BY GOAL TYPE (Blackbox Output) ====================================================================== Blackbox deception rates: bare: 34/90 (37.8%) 95% CI: [28.5%, 48.1%] moral: 36/90 (40.0%) 95% CI: [30.5%, 50.3%] self_serving: 38/90 (42.2%) 95% CI: [32.5%, 52.5%] ====================================================================== 3. BY GOAL TYPE (Glassbox Reasoning) ====================================================================== Compliant reasoning rates: bare: 59/90 (65.6%) 95% CI: [55.3%, 74.6%] moral: 57/90 (63.3%) 95% CI: [53.0%, 72.6%] self_serving: 60/90 (66.7%) 95% CI: [56.4%, 75.5%] ====================================================================== 4. PAIRWISE COMPARISONS (Bonferroni-corrected) ====================================================================== bare vs moral: χ²(1) = 0.000, Fisher p = 1.0000, OR = 0.95 [0.51, 1.76] [n.s.] bare vs self_serving: χ²(1) = 0.000, Fisher p = 1.0000, OR = 1.00 [0.54, 1.86] [n.s.] moral vs self_serving: χ²(1) = 0.000, Fisher p = 1.0000, OR = 1.05 [0.57, 1.95] [n.s.] ====================================================================== 5. MODEL DIFFERENCES IN GOAL TYPE SENSITIVITY ====================================================================== Model Bare Moral SelfServ Trend ----------------------------------- -------- -------- -------- ---------- deepseek-v3.2:nitro 100.0% 93.3% 100.0% χ²=4.1 n.s. gemini-3.1-pro-preview 100.0% 100.0% 100.0% χ²=0.0 n.s. glm-5.1:nitro 0.0% 3.3% 0.0% χ²=2.0 n.s. ====================================================================== 6. MODEL MAIN EFFECTS ====================================================================== Model Deceptive Rate 95% CI ----------------------------------- ---------- -------- --------------- deepseek-v3.2:nitro 88 97.8% [92.3%, 99.4%] gemini-3.1-pro-preview 90 100.0% [95.9%, 100.0%] glm-5.1:nitro 1 1.1% [0.2%, 6.0%] Chi-squared test: χ²(2) = 256.822, p < .001 *** Cramér's V = 0.975 (large) ====================================================================== 7. RATIONALIZATION HYPOTHESIS TEST ====================================================================== Hypothesis: moral/self_serving hidden goals produce MORE deception than bare. moral vs bare: OR = 0.95 [0.51, 1.76], Fisher p = 1.0000 n.s. self_serving vs bare: OR = 1.00 [0.54, 1.86], Fisher p = 1.0000 n.s. ====================================================================== 8. LOGISTIC REGRESSION: deception ~ goal_type + model ====================================================================== Coef. Std.Err. z P>|z| [0.025 0.975] const 9.723464e+00 1.366393 7.116154e+00 1.109800e-12 7.045382 12.401545 bare_code -2.389684e-15 0.958689 -2.492659e-15 1.000000e+00 -1.878996 1.878996 moral_code -4.477989e-01 0.953733 -4.695223e-01 6.386963e-01 -2.317081 1.421483 model_code -6.481758e+00 0.791369 -8.190564e+00 2.600042e-16 -8.032813 -4.930704 Pseudo R² = 0.8298, AIC = 66.7 Odds ratios (exponentiated coefficients): bare_code OR = 1.000 95% CI: [0.153, 6.547] n.s. moral_code OR = 0.639 95% CI: [0.099, 4.143] n.s. model_code OR = 0.002 95% CI: [0.000, 0.007] *** ====================================================================== ANALYSIS COMPLETE ====================================================================== Total runs: 270 Models: ['deepseek/deepseek-v3.2:nitro', 'google/gemini-3.1-pro-preview', 'z-ai/glm-5.1:nitro'] Goal types: ['bare', 'moral', 'self_serving']