Interpretable Adaptive Sampling for LLM Test-Time Scaling

Tue, 04 Aug 2026 00:00:00 +0000

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

Interpretable Adaptive Sampling for LLM Test-Time Scaling and Test-Time Compute

Tue, 04 Aug 2026 00:00:00 +0000

What Is LLM Test-Time Scaling?

Test-time scaling, also referred to as test-time compute or inference-time scaling, improves large language model reasoning by allocating additional computation during inference.

Adaptive Sampling for Test-Time Compute

Most fixed best-of-N approaches assign the same sampling budget to every prompt. Our approach dynamically allocates inference-time compute based on prompt complexity and model confidence.

Interpretable Inference-Time Compute

A lightweight fuzzy controller determines how many candidate responses each query receives. Easier or higher-confidence prompts receive fewer samples, while more difficult or uncertain prompts receive additional test-time compute.

Efficient LLM Reasoning

Adaptive sampling can reduce unnecessary candidate generation while maintaining competitive reasoning performance across question-answering and mathematical reasoning tasks.

Comparison with Best-of-N and Self-Consistency

We compare the approach with fixed best-of-N sampling, compute-aware test-time scaling, and self-certainty-based methods.

Our Paper

Test-time scaling, also known as test-time compute or inference-time scaling, can improve large language model reasoning by generating multiple candidate answers. However, most approaches assign the same sampling budget to every prompt, even though easy and difficult questions may require different amounts of inference-time compute.

In our paper, “Interpretable Adaptive Sampling for LLM Test-Time Scaling,” we introduce an adaptive method that determines how many candidate answers should be generated for each query.

The method uses a lightweight fuzzy controller that combines interpretable signals such as prompt complexity and model confidence. It assigns fewer samples to easier or higher-confidence prompts and more samples to difficult or uncertain prompts.

Our experiments compare this approach with fixed best-of-N sampling, compute-aware scaling, and self-certainty-based methods across question-answering and mathematical reasoning tasks.

The results show that adaptive fuzzy control can reduce the average number of generated samples while maintaining competitive reasoning performance.

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

Abstract

Test-time scaling improves LLM reasoning by generating and aggregating multiple candidate answers, yet many pipelines use fixed per-query budgets that spend the same compute on easy and difficult prompts. These fixed budgets are also difficult to inspect because they do not explain why a given prompt receives a particular number of samples.

We propose adaptive test-time scaling with a lightweight fuzzy controller that maps interpretable signals, including estimated prompt complexity and model confidence, to a per-query sampling budget. The controller assigns fewer samples to easier or more confident prompts and more samples to harder or less certain prompts, making inference-time compute inspectable rather than fixed or opaque.

We evaluate under a fair-alignment protocol with matched decoding settings and controlled answer selection, and compare against best-of-N, compute-aware scaling, and self-certainty-based baselines on question-answering and mathematical reasoning tasks. Across models and datasets, adaptive fuzzy control improves over several standard baselines and remains close to a selector-matched full-budget control while reducing the average number of samples.

These findings suggest that interpretable adaptive sampling is a practical direction for more efficient test-time reasoning in large language models.

Inference-Time Compute | Mobina Kashaniyan