Interpretable Adaptive Sampling for LLM Test-Time Scaling

This paper introduces an interpretable adaptive sampling framework for large language model test-time scaling.

Most test-time scaling approaches assign the same number of candidate generations to every prompt. However, easy and difficult prompts may require very different amounts of inference-time compute.

Our method uses a lightweight fuzzy controller that combines estimated prompt complexity and model confidence. It assigns fewer samples to easier or higher-confidence prompts and more samples to difficult or uncertain prompts.

The approach is evaluated against fixed best-of-N sampling, compute-aware scaling, and self-certainty-based methods on question-answering and mathematical reasoning tasks. The results demonstrate that adaptive fuzzy control can reduce the average number of generated samples while maintaining competitive reasoning performance.

This work is relevant to LLM test-time scaling, adaptive inference, compute-efficient AI, interpretable sampling, self-consistency, and efficient large language model reasoning.

Keywords: LLM test-time scaling, adaptive sampling, inference-time compute, efficient LLM inference, interpretable AI, fuzzy control, best-of-N sampling, self-consistency, adaptive computation, mathematical reasoning.

Citation

Kashaniyan, M., & Jannesari, A. (2026). Interpretable adaptive sampling for LLM test-time scaling. arXiv preprint arXiv:2608.03961. https://doi.org/10.48550/arXiv.2608.03961