受卡尔曼启发的混合推理系统运行时稳定性与恢复
Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems
AI总结：
本文提出了一种基于卡尔曼启发的混合推理系统运行时稳定性框架，通过监测创新信号和检测不稳定性，实现对任务失败前的可靠恢复。
摘要：
混合推理系统将学习组件与基于模型的推理相结合，越来越多地应用于工具增强的决策循环中，但其在部分可观测性和持续证据不匹配情况下的运行时行为仍缺乏深入理解。实际上，故障往往表现为内部推理动态的逐渐偏离，而非孤立的预测错误。本文从卡尔曼启发的角度研究混合推理系统的运行时稳定性。我们将推理建模为由内部创新信号驱动的随机推理过程，并引入认知漂移作为可测量的运行时现象。稳定性定义为可检测性、有界偏离和可恢复性，而非任务层面的正确性。我们提出了一种运行时稳定性框架，该框架监控创新统计信息，检测新兴的不稳定性，并触发具有恢复意识的控制机制。在多步、工具增强的推理任务上的实验显示，在任务失败之前能够可靠地检测到不稳定性，并表明当恢复可行时，可以在有限时间内重新建立有界内部行为。这些结果强调了运行时稳定性作为在不确定性下可靠推理的系统级要求。
英文摘要：
Hybrid reasoning systems that combine learned components with model-based inference are increasingly deployed in tool-augmented decision loops, yet their runtime behavior under partial observability and sustained evidence mismatch remains poorly understood. In practice, failures often arise as gradual divergence of internal reasoning dynamics rather than as isolated prediction errors. This work studies runtime stability in hybrid reasoning systems from a Kalman-inspired perspective. We model reasoning as a stochastic inference process driven by an internal innovation signal and introduce cognitive drift as a measurable runtime phenomenon. Stability is defined in terms of detectability, bounded divergence, and recoverability rather than task-level correctness. We propose a runtime stability framework that monitors innovation statistics, detects emerging instability, and triggers recovery-aware control mechanisms. Experiments on multi-step, tool-augmented reasoning tasks demonstrate reliable instability detection prior to task failure and show that recovery, when feasible, re-establishes bounded internal behavior within finite time. These results emphasize runtime stability as a system-level requirement for reliable reasoning under uncertainty.
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。
