Lookahead Compute Intervention Optimization for Diffusion Models: Predicting the Marginal Value of Future Computation for Adaptive Inference-Time Allocation

Diffusion models generate samples through iterative reverse processes that require repeated evaluations of a denoising network and, in some inference-time scaling methods, additional candidate generation, verification, or refinement. Conventional samplers generally follow predefined computational schedules, while adaptive methods allocate additional computation according to intermediate states, confidence estimates, search strategies, or predetermined criteria. However, the computational value of an operation depends on its downstream consequences: an additional evaluation may substantially improve a sample at one stage, provide little benefit at another, or consume resources that would be more valuable elsewhere. This paper proposes Lookahead Compute Intervention Optimization (LCIO), a conceptual framework for predicting the marginal downstream value of alternative computational interventions and allocating a finite inference budget accordingly. Rather than selecting only a noise intervention, LCIO selects among ordinary denoising, additional candidate evaluation, local refinement, optional state or feature reuse, reduced computation, and termination, subject to sampler compatibility and quality constraints. At each decision point, a lookahead evaluator estimates the expected final-generation utility attributable to each feasible intervention relative to a suitable counterfactual baseline. A budget-aware controller then selects interventions according to their predicted marginal utility, uncertainty, resource cost, and remaining budget. The framework distinguishes gross quality improvement from net improvement after accounting for computational overhead, and it supports both learned evaluation and limited simulation-based lookahead. This paper presents a mathematical formulation, an inference algorithm, evaluator-training objectives, a practical implementation strategy, and a controlled experimental protocol. Relevant prior research has already investigated diffusion inference-time scaling, search-based generation, adaptive computation, and global allocation of denoising evaluations. Consequently, LCIO does not claim novelty for adaptive compute allocation alone. Its central research hypothesis is narrower: explicitly predicting the counterfactual marginal downstream value of heterogeneous computational actions may improve the quality–compute trade-off compared with existing allocation policies under matched budgets. No experimental results are claimed in this paper.