Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems

• URL: http://arxiv.org/abs/2602.15855v1
• Date: Sat, 24 Jan 2026 11:29:57 GMT
• Title: Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems
• Authors: Barak Or,
• Abstract summary: We study stability in hybrid reasoning systems from a Kalman-inspired perspective.<n>We propose a runtime stability framework that monitors innovation statistics, detects emerging instability, and triggers recovery-aware control mechanisms.
• Score: 1.9544213396776273
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Stability as a Liability:Systematic Breakdown of Linguistic Structure in LLMs [5.96875296117642]
  We show that stable parameter trajectories lead stationary solutions to minimize the forward KL divergence to the empirical distribution.<n>We empirically validate this effect using a controlled feedback-based training framework.<n>It indicates that optimization stability and generative expressivity are not inherently aligned, and that stability alone is an insufficient indicator of generative quality.
  arXiv  Detail & Related papers  (2026-01-26T15:34:50Z)
• From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models [77.04403907729738]
  This survey charts the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior.<n>We demonstrate how uncertainty is leveraged as an active control signal across three frontiers.<n>This survey argues that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.
  arXiv  Detail & Related papers  (2026-01-22T06:21:31Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Noradrenergic-inspired gain modulation attenuates the stability gap in joint training [44.99833362998488]
  Studies in continual learning have identified a transient drop in performance on mastered tasks when assimilating new ones, known as the stability gap.<n>We argue that it reflects an imbalance between rapid adaptation and robust retention at task boundaries.<n>Inspired by locus coeruleus mediated noradrenergic bursts, we propose uncertainty-modulated gain dynamics.
  arXiv  Detail & Related papers  (2025-07-18T16:34:06Z)
• Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift? [51.12297424766236]
  AURORA is a framework to evaluate malware classifiers based on their confidence quality and operational resilience.<n>AURORA is complemented by a set of metrics designed to go beyond point-in-time performance.<n>The fragility in SOTA frameworks across datasets of varying drift suggests the need for a return to the whiteboard.
  arXiv  Detail & Related papers  (2025-05-28T20:22:43Z)
• Stability-Certified Learning of Control Systems with Quadratic Nonlinearities [9.599029891108229]
  This work primarily focuses on an operator inference methodology aimed at constructing low-dimensional dynamical models. Our main objective is to develop a method that facilitates the inference of quadratic control dynamical systems with inherent stability guarantees.
  arXiv  Detail & Related papers  (2024-03-01T16:26:47Z)
• Continual evaluation for lifelong learning: Identifying the stability gap [35.99653845083381]
  We show that a set of common state-of-the-art methods still suffers from substantial forgetting upon starting to learn new tasks. We refer to this intriguing but potentially problematic phenomenon as the stability gap. We establish a framework for continual evaluation that uses per-iteration evaluation and we define a new set of metrics to quantify worst-case performance.
  arXiv  Detail & Related papers  (2022-05-26T15:56:08Z)
• Bayesian Algorithms Learn to Stabilize Unknown Continuous-Time Systems [0.0]
  Linear dynamical systems are canonical models for learning-based control of plants with uncertain dynamics. A reliable stabilization procedure for this purpose that can effectively learn from unstable data to stabilize the system in a finite time is not currently available. In this work, we propose a novel learning algorithm that stabilizes unknown continuous-time linear systems.
  arXiv  Detail & Related papers  (2021-12-30T15:31:35Z)
• Training Generative Adversarial Networks by Solving Ordinary Differential Equations [54.23691425062034]
  We study the continuous-time dynamics induced by GAN training. From this perspective, we hypothesise that instabilities in training GANs arise from the integration error. We experimentally verify that well-known ODE solvers (such as Runge-Kutta) can stabilise training.
  arXiv  Detail & Related papers  (2020-10-28T15:23:49Z)
• Efficient Empowerment Estimation for Unsupervised Stabilization [75.32013242448151]
  empowerment principle enables unsupervised stabilization of dynamical systems at upright positions. We propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel. We show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images.
  arXiv  Detail & Related papers  (2020-07-14T21:10:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.