Lookahead Feature-Recomputation Intervention Optimization for Diffusion Models: Predicting Downstream Generation Quality to Select Adaptive Feature Reuse and Recomputation Actions

Diffusion models generate samples through iterative denoising processes that repeatedly execute computationally expensive neural networks. Because intermediate representations often exhibit temporal redundancy across denoising steps, feature caching and reuse can reduce inference cost by avoiding redundant computation. However, a cached feature that remains numerically similar to its previous value may nevertheless become important to recompute if its approximation error propagates through subsequent network layers and affects the final generated sample. Conversely, recomputing a feature with substantial local variation may provide little practical benefit when its downstream influence is limited. This paper proposes Lookahead Feature-Recomputation Intervention Optimization (LFRIO), a conceptual framework for selecting feature-reuse and recomputation actions according to their predicted downstream consequences. Rather than relying exclusively on fixed caching schedules, feature similarity, or predetermined thresholds, LFRIO treats feature computation as a sequential decision problem. At each eligible denoising step, a controller considers three principal actions: reuse a cached feature, partially recompute selected feature components, or fully recompute the feature. A lookahead evaluator estimates the resulting downstream generation quality, approximation error, computational cost, and prediction uncertainty. A decision mechanism then selects an action according to its predicted quality–efficiency utility under a computational budget. The framework supports layer-wise, token-wise, channel-wise, spatially localized, and hierarchical feature interventions, provided that the selected architecture permits valid substitutions and partial execution. This paper presents a mathematical formulation, a training strategy for the evaluator, an inference algorithm, and an experimental protocol designed to distinguish the value of downstream prediction from the benefits of adaptive caching alone. Existing methods, including DeepCache, FORA, token-wise feature caching, and more recent constraint-aware caching approaches, establish substantial prior work in this area. Consequently, the central research hypothesis is not that adaptive feature reuse is new, but that explicit prediction of the downstream consequences of alternative recomputation actions can improve the quality–efficiency trade-off beyond strong similarity-based, threshold-based, and adaptive caching baselines. No experimental results are claimed in this paper.