An Implementation of Autonomous Reasoning in Large Language Models through Token-Level Inner Dialogue Mechanisms

Autonomous reasoning systems often face limitations when addressing complex, multi-step tasks that require continuous self-assessment and adaptation. A novel framework for reasoning enhancement introduces token-level inner dialogue, allowing models to recursively evaluate and refine their outputs during the generation process. This mechanism promotes deeper logical coherence and adaptive problem-solving through iterative token interactions, leading to improved performance across reasoning tasks, including ethical decision-making, logical progression, and abstract reasoning. Extensive experimental results demonstrate the effectiveness of this approach, highlighting substantial improvements in reasoning accuracy, consistency, and task completion rates compared to baseline models. The introduction of recursive feedback loops within token generation proves instrumental in maintaining logical coherence, particularly in tasks with high levels of complexity. Challenges related to computational overhead and architectural complexity were identified, yet the benefits of deeper token-level reasoning establish this framework as a significant advancement for autonomous reasoning in artificial intelligence.