Cognitive Chain Optimization: A Dual-Process Reinforcement Framework for Explainable LLM Reasoning
Keywords:
explainable AI, large language models, dual-process theory, reinforcement learning, cognitive architecture, reasoning transparency, AI governance, socio-technical systemsAbstract
The rapid advancement of large language models (LLMs) has produced systems capable of sophisticated language generation, yet their reasoning processes remain largely opaque, presenting significant challenges for trust, verification, and governance in high-stakes applications. This paper proposes a novel framework, Cognitive Chain Optimization (CCO), which integrates dual-process cognitive theory with reinforcement learning to construct explainable reasoning trajectories in LLMs. The framework distinguishes between two complementary reasoning modalities: an intuitive, associative System One for rapid heuristic generation, and a deliberative, analytical System Two for structured logical verification. Reinforcement learning is employed not merely to optimize final outputs, but to shape the cognitive chain itself, rewarding intermediate reasoning steps that are both accurate and interpretable. We argue that this approach addresses fundamental structural trade-offs between reasoning depth, computational efficiency, and model transparency. The paper examines the architectural implications of implementing such a dual-system within transformer-based models, considering both modular and integrated design patterns. We analyze the governance and policy dimensions of deployable explainable AI systems, emphasizing the need for auditability, fairness, and robustness in reasoning chains. Sustainability challenges related to the computational overhead of dual-process inference are discussed alongside potential infrastructure solutions, including hierarchical caching and context-aware resource allocation. Cross-domain comparisons with human cognitive science and classical symbolic AI illuminate the theoretical foundations of the proposed framework. A case illustration in medical diagnostic reasoning demonstrates the practical viability of CCO. Finally, we outline forward-looking perspectives on the evolution of explainable reasoning agents and their role in socio-technical infrastructures. This paper contributes a systems-level perspective on aligning LLM reasoning processes with human interpretability requirements, offering a foundation for future research in explainable artificial intelligence, cognitive architectures, and AI governance.
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