Lookahead Noise Intervention Optimization (LNIO): Fifth Experimental Report - Predictive Lookahead and Stochastic Exploration for Compute-Efficient Diffusion Sampling

This fifth experimental report presents an expanded evaluation of Lookahead Noise Intervention Optimization (LNIO), investigating the effects of lookahead frequency, rank-conditioned stochastic selection, probability penalization, and hybrid predictive–direct intervention on text-to-image diffusion sampling. The experiment evaluates 36 methods across 30 prompts and two random seeds, yielding 2,160 planned prompt–seed–method conditions. All methods are evaluated using Stable Diffusion v1.5 at 512 × 512 resolution with eight denoising steps. ImageReward is adopted as the primary final-image quality metric, while CLIP similarity serves as a supplementary metric. The evaluated strategies include baseline sampling, adaptive intervention, direct random intervention, multiple lookahead intervals, Bias0 and Bias10 rank-selection policies, alternative Top-1 penalization configurations, alternating Top-2/Top-4 policies, and a hybrid method combining an initial lookahead decision with subsequent direct stochastic interventions. The highest descriptive ImageReward mean is obtained by Hybrid E8 Penalized Top-1 + Random Direct E2 (−0.0379), followed by Adaptive (−0.0423) and E8 Bias9 Top-1 (−0.0464). The hybrid method also obtains the highest reported CLIP mean (0.3498). However, prompt-clustered paired tests and Holm correction do not establish statistically significant improvements for the principal comparisons. The findings therefore identify a promising hybrid configuration and several informative trends, but do not establish general superiority. In particular, the experiment suggests that increasing lookahead frequency does not necessarily improve image quality in proportion to its computational cost. The report discusses these findings, the statistical evidence, and the limitations that should guide subsequent LNIO experiments.