Coherence Monitoring for Neural Systems using Cₖ Dynamics: An Eidionic Coprocessor Concept

Modern neural systems such as large language models and reinforcement learning agents exhibit impressive capabilities but often lack mechanisms for monitoring internal coherence during reasoning and decision processes. Failures such as hallucinations, unstable reasoning chains, and incoherent agent behavior may arise when internal state transitions become structurally difficult to integrate. This paper proposes a lightweight monitoring framework based on the concept of Coherence-Complexity (Cₖ), originally introduced in the context of the Eidionic theory of adaptive systems. The proposed approach treats Cₖ as a structural measure of integration effort within a system's state space. Instead of modifying the underlying AI architecture, an external Eidionic Coprocessor continuously observes internal state trajectories and computes Cₖ dynamics as a stability indicator. Rising Cₖ gradients signal increasing integration difficulty and potential coherence breakdown. The proposed framework can be implemented as a software module running alongside existing models and may provide early indicators for hallucinations, reasoning instability, and multi-agent conflict states. This paper outlines the theoretical basis, system architecture, and possible experimental pathways for evaluating coherence monitoring in neural systems.