Box Maze: A Process-Control Architecture for Reliable LLM Reasoning
2026-03-19 • Artificial Intelligence
Artificial IntelligenceComputation and Language
AI summaryⓘ
The authors studied how large language models (LLMs) sometimes make mistakes or guess wrong when given tricky questions. They propose a new design called the Box Maze framework, which breaks down the model's thinking into three clear parts to keep it more accurate. They tested this idea in simulations with several different LLMs and found that adding these structured control layers helped reduce errors from about 40% to less than 1% when the models faced difficult prompts. Although these results are preliminary and based on simulations, the authors suggest this approach could make LLMs reason more reliably.
large language modelshallucinationadversarial promptingreinforcement learning from human feedback (RLHF)process control architecturememory groundingstructured inferenceboundary enforcementsimulation evaluationcognitive control layers
Authors
Zou Qiang
Abstract
Large language models (LLMs) demonstrate strong generative capabilities but remain vulnerable to hallucination and unreliable reasoning under adversarial prompting. Existing safety approaches -- such as reinforcement learning from human feedback (RLHF) and output filtering -- primarily operate at the behavioral level and may lack explicit architectural mechanisms for enforcing reasoning process integrity. This paper proposes the Box Maze framework, a conceptual process-control architecture that decomposes LLM reasoning into three explicit layers: memory grounding, structured inference, and boundary enforcement. We introduce preliminary simulation-based evaluation involving progressive boundary erosion scenarios across multiple heterogeneous LLM systems (DeepSeek-V3, Doubao, Qwen). Results from n=50 adversarial scenarios suggest that explicit cognitive control layers may improve consistency in boundary maintenance, with architectural constraints reducing boundary failure rates from approximately 40% (baseline RLHF) to below 1% under adversarial conditions. While current validation is simulation-based, these preliminary results indicate that process-level control may offer a promising direction for improving reliability in large language model reasoning.