这篇论文的PUMA框架能实时诊断大模型推理时是有效思考还是绕圈子，在1.5B到32B模型上准确率更高且更省计算，推荐给关心推理效率的朋友。
PUMA提出相位-动量对齐假设与认知能量模型，以诊断大推理模型（LRM）在测试时缩放中的过度思考问题。与易受欺骗性收敛的信息论方法不同，PUMA通过轻量级相位监测和事件触发几何分析，实时区分主动探索与被动停滞。在1.5B至32B参数的LRM上，PUMA在多个基准上实现了更优的准确率-效率权衡，优于现有基线方法。作为训练无关框架，PUMA无需额外训练即可自适应截断或矫正推理过程。
Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment
Test-time scaling empowers Large Reasoning Models (LRMs) to tackle complex tasks via extensive Chain-of-Thought (CoT). However, this often induces the "overthinking" paradox, where redundant reasoning increases computational overhead without guaranteeing accuracy. Existing test-time efficiency optimization methods primarily fall into two categories: information-theoretic approaches, which are prone to "deceptive convergence" where low uncertainty masks hallucinations, and latent representation analyses, which are often post-hoc, lacking the real-time sensitivity for dynamic reasoning. To bridge this gap, we first posit the Phase-Momentum Alignment Hypothesis, asserting that reasoning correctness hinges on the temporal synchronization between geometric momentum and uncertainty resolution. We then theoretically formulate the Cognitive-Energy Model to characterize these dynamics through two orthogonal dimensions: Geometric Cognitive Effort, quantified by latent velocity and tortuosity, and Entropic Cognitive Uncertainty. To operationalize this, we introduce PUMA (Phase-Uncertainty Momentum Alignment), a training-free framework employing a tiered diagnostic architecture. By coupling lightweight phase monitoring with event-triggered geometric analysis, PUMA effectively distinguishes active exploration from passive stagnation, enabling precise interventions through adaptive truncation or corrective measures. Extensive experiments on LRMs spanning 1.5B to 32B demonstrate that PUMA consistently outperforms state-of-the-art baselines across diverse benchmarks, achieving a superior accuracy-efficiency trade-off and robust cross-domain generalization.