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.

翻译：大型语言模型展现出强大的生成能力，但在对抗性提示下仍易出现幻觉和不可靠推理。现有安全方法——如基于人类反馈的强化学习（RLHF）和输出过滤——主要作用于行为层面，可能缺乏强制执行推理过程完整性的显式架构机制。本文提出盒状迷宫框架，这是一种概念性过程控制架构，将LLM推理分解为三个显式层级：记忆基础化、结构化推理和边界强制。我们引入初步的基于模拟的评估，涉及跨多个异构LLM系统（DeepSeek-V3、Doubao、Qwen）的渐进式边界侵蚀场景。来自n=50个对抗性场景的结果表明，显式认知控制层可能改善边界维护的一致性，架构约束使对抗条件下的边界失效概率从约40%（基线RLHF）降至1%以下。尽管当前验证基于模拟，这些初步结果表明过程级控制可能为提升大型语言模型推理可靠性提供有前景的方向。