Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems

2026-02-19 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

Barak Or from Google and Reichman Tech School introduces a Kalman-inspired framework for managing runtime stability and recovery in hybrid reasoning systems. These systems, which combine learned components with model-based inference, often experience failures as gradual divergence of internal reasoning dynamics rather than isolated errors. The framework models reasoning as a stochastic inference process driven by an internal "innovation signal" and defines "cognitive drift" as a measurable runtime phenomenon. Stability is characterized by detectability, bounded divergence, and recoverability, moving beyond task-level correctness. Experiments on multi-step, tool-augmented reasoning tasks using the HotpotQA dataset demonstrate that this framework reliably detects instability before task failure, and when recovery is possible, it re-establishes bounded internal behavior within finite time, specifically a Mean Time to Recovery for Agentic Systems (MTTR-A) of 4.00 ± 0.82 steps.

Key takeaway

For AI Scientists and Research Scientists developing or deploying hybrid reasoning systems, focusing solely on task-level correctness is insufficient. You should integrate runtime stability monitoring, specifically tracking innovation signals and cognitive drift, to detect internal inconsistencies early. This approach allows for timely intervention through recovery mechanisms like tool fallback and gain modulation, preventing silent divergence from task intent and ensuring more trustworthy system behavior under real-world uncertainty.

Key insights

Runtime stability in hybrid reasoning systems requires monitoring internal dynamics for cognitive drift and enabling recovery.

Principles

• Stability is a dynamic, runtime property.
• Detectability and recoverability are distinct properties.
• Adaptation can mask semantic divergence.

Method

The proposed method monitors innovation statistics to detect cognitive drift, then triggers recovery actions like tool fallback, gain modulation, and rollback to re-establish bounded internal behavior.

In practice

• Monitor innovation signals for early instability detection.
• Implement tool fallback for persistent evidence mismatch.
• Use gain modulation to regulate evidence influence.

Topics

• Hybrid Reasoning Systems
• Runtime Stability
• Kalman Filtering
• Cognitive Drift
• Recovery Mechanisms

Best for: AI Scientist, Research Scientist, AI Researcher, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.