这篇论文告诉你为什么监控量化的DeepSeek小模型不能用token概率，还给出了一个更靠谱的校准方法，真正提升了准确率。
论文指出低比特量化使得小推理模型如DeepSeek-R1-Distill-Qwen-1.5B成本降低但可能削弱推理链，传统使用token对数概率增量作为解码监控指标是无效的，因其在模型自身采样下是均值为零的鞅，无法反映轨迹健康度。作者提出一种训练无关的解码控制器，结合退化感知报警分数和校准的e-process时序检测器。在GSM8K上测试FP16和INT4版本，原始监控触发93-95%生成，校准后变成选择性检测失败轨迹（φ≈0.3，精度约0.6，基准0.38）。INT4准确率从63%提升至69%（配对McNemar p=0.18，n=100），代价为28% token预算。
Calibrated e-CUSUM Decoding for Quantized Reasoning Models: Why Token Log-Probability Is the Wrong Observable for Decoding Monitors
Low-bit quantization makes small reasoning models inexpensive to deploy but can degrade their chains of thought. This motivates decoder-side monitors that intervene when generation becomes unreliable. We show that a natural candidate, the centered token log-probability increment $\log p(w_t)+H_t$, is the wrong observable for this purpose. Under the model's own sampling law it is a mean-zero martingale by construction, so it measures sampling self-consistency rather than trajectory health and is nearly silent during confident repetition, where both $\log p(w_t)$ and entropy are close to zero. We introduce a training-free decoding controller that combines (i) a degeneration-aware alarm score fusing token uncertainty with explicit verbatim repetition and (ii) a calibrated e-process-inspired sequential detector. The raw product process is Ville-valid under a conditional-mean null, while the deployed CUSUM-floored statistic is treated as an empirical change detector because the score is history-dependent and autocorrelated. On GSM8K with DeepSeek-R1-Distill-Qwen-1.5B in FP16 and INT4, calibration turns a monitor that fires on 93--95% of generations into a selective detector of failing traces ($φ\approx 0.3$, precision $\approx 0.6$ against a 0.38 base rate). In this pilot, the controller reduces measured verbatim-degeneration signals and yields a positive but statistically inconclusive INT4 accuracy change from 63% to 69% (paired McNemar $p=0.18$, $n=100$), at a 28% token-budget cost. We also find that non-termination, rather than looping, is the dominant failure mode on GSM8K. The main contribution is methodological: an explanation of why centered token log-probability is inadequate for decoder monitoring and a calibrated, cautiously evaluated replacement.