这篇把测试时算力按题分配：难题多算、简单题少算，还比固定预算省采样。
arXiv论文2608.03961提出用轻量模糊控制器为每条查询动态分配采样预算。控制器依据提示复杂度和模型置信度，对简单或高置信度查询少采样，对困难或低置信度查询多采样。在问答和数学推理任务中，它与best-of-N、计算量感知缩放、自置信度基线对比，效果接近全预算控制，同时降低平均采样数。论文2608.03961称这套机制让推理时的计算量可解释，不再固定或不透明。
Interpretable Adaptive Sampling for LLM Test-Time Scaling
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.