Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems
A runtime-stability framework for detecting cognitive drift, measuring innovation, and reporting recovery or unrecovered failure in tool-augmented reasoning systems.
Abstract
Tool-augmented reasoning systems can remain locally coherent while adapting to stale, delayed, or misrouted evidence. This manuscript frames that behavior as a runtime-stability problem rather than a final-answer accuracy problem. It introduces a Kalman-inspired operational vocabulary for observing prediction, realized evidence, innovation, drift, recovery, and unrecovered failure within finite reasoning traces, and evaluates a lightweight monitor under controlled HotpotQA evidence-mismatch perturbations. The results show that innovation monitoring can detect drift in 79.2% of perturbed episodes, while sustained corruption often prevents recovery, motivating explicit reporting of unrecovered runtime failures.
Key Innovations
Frames agent failures as observable trace dynamics: prediction, realized evidence, innovation, drift, recovery, and unrecovered failure.
Defines finite-horizon drift monitoring that separates local innovation mismatch from semantic task preservation.
Reports detection, recovery rate, MTTR-A, and unrecovered failures separately, showing that reliable detection does not guarantee safe recovery.