我可能没有表达清楚：在推理时间诊断LLM推理中的动态不稳定性
"I May Not Have Articulated Myself Clearly": Diagnosing Dynamic Instability in LLM Reasoning at Inference Time
AI总结：
研究通过分析LLM推理过程中的动态不稳定性，发现早期不稳定性可能预示后续正确答案，而晚期不稳定性更易导致失败，提出基于分布偏移和熵的诊断方法。
摘要：
大型语言模型（LLM）中的推理失败通常只在生成结束时进行测量，然而许多失败表现为过程层面的崩溃：模型在推理过程中
英文摘要：
Reasoning failures in large language models (LLMs) are typically measured only at the end of a generation, yet many failures manifest as a process-level breakdown: the model "loses the thread" mid-reasoning. We study whether such breakdowns are detectable from inference-time observables available in standard APIs (token log probabilities), without any training or fine-tuning. We define a simple instability signal that combines consecutive-step distributional shift (JSD) and uncertainty (entropy), summarize each trace by its peak instability strength, and show that this signal reliably predicts failure. Across GSM8K and HotpotQA, instability strength predicts wrong answers with above-chance AUC and yields monotonic bucket-level accuracy decline at scale across model sizes. Crucially, we show that instability is not uniformly harmful: early instability can reflect subsequent stabilization and a correct final answer (\emph{corrective instability}), whereas late instability is more often followed by failure (\emph{destructive instability}), even at comparable peak magnitudes, indicating that recoverability depends not only on how strongly the distribution changes but also on when such changes occur relative to the remaining decoding horizon. The method is model-agnostic, training-free, and reproducible, and is presented as a diagnostic lens rather than a corrective or control mechanism.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
