Lookahead Noise Intervention Optimization (LNIO): Third Experimental Report — An Expanded Evaluation of Preview-Ranked Stochastic Sampling Policies in Diffusion Models

This report presents the third experimental investigation of Lookahead Noise Intervention Optimization (LNIO), an adaptive sampling framework that evaluates candidate stochastic interventions through preview-based estimates of downstream generation quality. Building on the preceding investigations, this experiment expands the evaluation to 50 sampling configurations, 27 text prompts, and five random seeds, producing 6,750 completed experimental conditions. The experiment examines several dimensions of preview-guided stochastic sampling, including decision frequency, probability bias, rank-based candidate selection, and computational overhead. Stable Diffusion 1.5 is used as the generative model, with CLIP-based preview evaluation guiding candidate ranking and ImageReward serving as the primary evaluation metric. Final CLIP scores are reported as a secondary metric. The descriptive results show that the adaptive heuristic obtains the highest mean ImageReward score (−0.3036), while several LNIO configurations also appear among the strongest-performing stochastic sampling policies. In particular, the E4 configuration with Bias10 achieves a mean ImageReward score of −0.4045, followed by E4-Bias12.5 at −0.4094. Both are descriptively higher than the baseline score of −0.4426. The results also demonstrate that LNIO provides a configurable relationship between preview frequency and computational expenditure: E8, E4, and E2 require approximately 11, 14, and 20 UNet forward calls, respectively, compared with eight calls for the baseline and direct-random methods. However, paired statistical comparisons with prompt-level clustering and Holm correction do not establish statistically significant differences among the tested configurations. The findings should therefore be interpreted as exploratory evidence about the behavior, flexibility, and potential utility of preview-ranked stochastic sampling rather than as proof of universal performance superiority.