用卡尔曼滤波思想提升混合推理系统的运行稳定性。
Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems
- 将推理过程建模为受内部创新信号驱动的随机推断
- 可提前检测到系统失稳，恢复后能在有限时间内重新稳定
- 适合部署在工具增强型决策系统中需长期可靠运行的场景
结合学习组件与基于模型推理的混合推理系统在工具增强型决策循环中日益普及，但其在部分可观测性与持续证据不一致下的运行行为仍缺乏理解。实践中，失败常表现为内部推理动态的渐进发散，而非孤立预测错误。本文从卡尔曼滤波视角研究混合推理系统的运行稳定性，将推理建模为由内部创新信号驱动的随机推断过程，并引入可度量的“认知漂移”作为运行时现象。稳定性定义为可检测性、有界发散性和可恢复性，而非任务级正确性。提出一种运行时稳定性框架，通过监控创新统计量，检测潜在不稳定性，并触发具备恢复意识的控制机制。在多步、工具增强型推理任务上的实验表明，该框架可在任务失败前可靠检测不稳定性，且在可行情况下，恢复能于有限时间内重建有界内部行为。结果强调了运行稳定性是不确定环境下可靠推理的系统级要求。
原文摘要 · Abstract (English)
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.
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