Large language models increasingly spend inference compute sampling multiple chain-of-thought traces or searching over merged checkpoints. This shifts the bottleneck from generation to selection, often without supervision on the target distribution. We show entropy-based exploration proxies follow an inverted-U with accuracy, suggesting extra exploration can become redundant and induce overthinking. We propose NEX, a white-box label-free unsupervised scoring framework that views reasoning as alternating E-phase (exploration) and X-phase (exploitation). NEX detects E-phase as spikes in newly activated MLP neurons per token from sparse activation caches, then uses a sticky two-state HMM to infer E-X phases and credits E-introduced neurons by whether they are reused in the following X span. These signals yield interpretable neuron weights and a single Good-Mass Fraction score to rank candidate responses and merged variants without task answers. Across reasoning benchmarks and Qwen3 merge families, NEX computed on a small unlabeled activation set predicts downstream accuracy and identifies better variants; we further validate the E-X signal with human annotations and provide causal evidence via "Effective-vs-Redundant" neuron transfer.

翻译：大型语言模型在推理过程中越来越多地消耗计算资源来采样多条思维链轨迹或搜索合并后的检查点。这使得瓶颈从生成转移到了选择环节，且通常缺乏目标分布上的监督信息。我们证明基于熵的探索代理指标与准确率呈倒U型关系，表明过度的探索可能变得冗余并引发过度思考。我们提出了NEX，一种白盒、无标签、无监督的评分框架，将推理过程视为探索阶段与利用阶段的交替。NEX通过稀疏激活缓存中每个token新激活的MLP神经元峰值来检测探索阶段，随后采用粘性双状态隐马尔可夫模型推断探索-利用阶段，并根据探索阶段引入的神经元是否在后续利用区间中被重用来对其进行赋分。这些信号可生成可解释的神经元权重及单一“优质质量分数”指标，用于在无需任务答案的情况下对候选响应及合并变体进行排序。在多项推理基准测试及Qwen3合并模型族上的实验表明，基于少量无标签激活集计算的NEX分数能够有效预测下游准确率并识别更优变体；我们进一步通过人工标注验证了探索-利用信号的有效性，并通过“有效-冗余”神经元迁移实验提供了因果性证据。