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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']
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