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

• URL: http://arxiv.org/abs/2603.19182v1
• Date: Thu, 19 Mar 2026 17:41:18 GMT
• Title: Box Maze: A Process-Control Architecture for Reliable LLM Reasoning
• Abstract summary: This paper proposes a conceptual process-control architecture that decomposes large language models into three explicit layers: memory grounding, structured inference, and boundary enforcement.<n>Preliminary results indicate that process-level control may offer a promising direction for improving reliability in large language model reasoning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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