FERE-CRS: A Validated Cognitive Architecture for Emergent, Fluid Reasoning via Active Inference

A fundamental challenge in artificial intelligence is the creation of systems capable of fluid reasoning—the ability to solve novel, complex problems in a principled and adaptive manner. This paper introduces and empirically validates the Fluid Emergent Reasoning Engine (FERE-CRS), a novel cognitive architecture designed to foster such capabilities. The architecture is grounded in the Free Energy Principle (FEP) and its process theory, Active Inference (AIF), which posit that intelligent agents maintain their existence by minimizing surprise through a continuous cycle of perception and action. We address the computational intractability of applying AIF to complex, neuro-symbolic systems by proposing the Cognitive Resonance Score (CRS), a tractable, multi-component heuristic proxy for Variational Free Energy (VFE). The CRS serves as a "common currency" for a Meta-Reasoning Agent (MRA) to orchestrate the functions of heterogeneous components, including a Large Language Model (LLM) acting as a versatile cognitive engine. We detail a "cognitive choreographer" methodology that makes the validation of this complex architecture tractable and transparent. In a 500-trial experiment on a complex, inferential artifact analysis task, the FERE-CRS agent produced significantly higher-quality explanations (mean score: 8.48/10) and was five times more efficient (mean cost: 610.0 units) than a strong Retrieval-Augmented Generation (RAG) baseline (mean score: 6.71/10; mean cost: 3000.0 units). Furthermore, analysis of the agent’s reasoning trace provides quantitative evidence of AIF-consistent adaptive behavior, demonstrating a clear shift from information-seeking to goal-directed action as uncertainty is resolved. Crucially, we argue that this architecture creates the conditions for transfer learning, enabling the agent to learn abstract, generalizable cognitive strategies that can be applied to entirely novel problems, a key shortcoming of current AI paradigms. This work provides a complete theoretical, architectural, and empirical account of a novel approach to building more intelligent, adaptive, and explainable AI systems.