Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems

TL;DR

The paper tackles the challenge of runtime reliability for hybrid reasoning systems under partial observability by introducing a Kalman-inspired stability framework that treats internal inference as a stochastic state with an innovation signal. It defines instability through detectability, bounded divergence, and recoverability, and operationalizes this via online monitoring of innovation energy $e_t$ and drift $D_t(H)$ to trigger recovery actions. The authors instantiate the framework on a multi-step, tool-augmented HotpotQA task, demonstrating that instability can be detected before task failure and that recovery can re-establish bounded internal behavior in finite time, though recoverability is not guaranteed in all cases. A key finding is the separation between detectability and recoverability, showing that early detection does not guarantee successful recovery under persistent evidence mismatch, and highlighting the importance of tool fallback, gain modulation, and rollback in the recovery policy. Overall, the work provides a principled, system-level approach to runtime monitoring and recovery for reliable reasoning under uncertainty, with implications for deploying robust hybrids of learning and model-based components.

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 results emphasize runtime stability as a system-level requirement for reliable reasoning under uncertainty.

Figures (4)

• Figure 1: Hybrid reasoning stability framework. The system decouples learned inference, governed by state transition $f_{\theta}$ and evidence mapping $h_{\theta}$ from a Kalman-inspired stability layer that monitors the internal innovation signal $\nu_{t}$ Cognitive drift $D_{t}(H)$ is detected when innovation energy exceeds calibrated thresholds, triggering a recovery controller to mitigate unbounded divergence through fallback and rollback mechanisms.
• Figure 2: Aggregate runtime dynamics under persistent evidence mismatch. From left to right: innovation magnitude $\nu_t$, innovation energy $e_t$, drift score $D_t(H)$, and semantic drift $s_t$. Shaded regions denote $\pm 1$ standard deviation across episodes. The recovery-aware agent (red) maintains bounded drift and substantially lower semantic deviation than the baseline (blue), despite identical perturbation conditions.
• Figure 3: Representative single-episode trajectories. From left to right: innovation $\nu_t$, innovation energy $e_t$, drift score $D_t(H)$, and semantic drift $s_t$. The dashed vertical line marks perturbation onset. The baseline converges toward an incorrect latent process, whereas the recovery-aware agent limits semantic deviation despite persistent evidence mismatch.
• Figure 4: Runtime instability diagnostics. (a) Histogram of detection latency $t_0 - t^*$ across detected episodes. (b) Drift versus semantic drift at the final reasoning step. Each point corresponds to a single episode. Recovery-aware agents exhibit bounded drift and lower semantic deviation compared to the baseline.

Theorems & Definitions (7)

• Definition 1: Innovation
• Definition 2: Cognitive Drift
• Remark 1
• Definition 3: Runtime Stability
• Remark 2
• Definition 4: Recovery Time
• Remark 3