Box Maze: A Process-Control Architecture for Reliable LLM Reasoning
Researchers have proposed Box Maze, a process-control architecture designed to mitigate hallucinations and unreliable reasoning in LLMs. Preliminary results show it can reduce reasoning failure rates from 40% to less than 1% under adversarial conditions.
Computer Science > Artificial Intelligence
Title:Box Maze: A Process-Control Architecture for Reliable LLM Reasoning
View PDF HTML (experimental)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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Source: arXiv cs.AI Recent
