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[2602.15855] Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems
Summary
This article presents a framework for ensuring runtime stability and recovery in hybrid reasoning systems, emphasizing the importance of monitoring internal dynamics to prevent failures in decision-making processes.
Why It Matters
As hybrid reasoning systems become more prevalent in decision-making, understanding their runtime behavior under uncertainty is crucial. This research addresses the gap in knowledge regarding stability and recovery mechanisms, which can enhance the reliability of AI systems in real-world applications.
Key Takeaways
- Introduces a Kalman-inspired framework for monitoring stability in hybrid reasoning systems.
- Defines stability in terms of detectability, bounded divergence, and recoverability.
- Demonstrates that early detection of instability can prevent task failures.
- Recovery mechanisms can restore system stability within a finite time.
- Highlights the need for runtime stability as a critical requirement for reliable AI reasoning.
Computer Science > Machine Learning arXiv:2602.15855 (cs) [Submitted on 24 Jan 2026] Title:Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems Authors:Barak Or View a PDF of the paper titled Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems, by Barak Or View PDF HTML (experimental) Abstract: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 re...
