Box Maze: A Process-Control Architecture for Reliable LLM Reasoning

This paper introduces Box Maze, a new architectural framework designed to make large language models more reliable and resistant to manipulation by breaking down their reasoning process into three explicit layers: memory grounding, structured inference, and boundary enforcement. The researchers tested this approach against adversarial attacks and found it dramatically reduced failure rates from about 40% down to below 1% compared to standard safety methods. The work suggests that controlling how LLMs think internally, rather than just filtering outputs, could be a better way to prevent unreliable reasoning and hallucinations.