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

Large language models (LLMs) have made significant advancements in the field of natural language processing in recent years. Their powerful generative capabilities have led to widespread applications across various domains. However, LLMs are prone to hallucinations and unreliable reasoning under adversarial prompting, which is particularly concerning in high-stakes applications. Existing safety methods, such as Reinforcement Learning from Human Feedback (RLHF) and output filtering, primarily operate at the behavioral level and lack explicit architectural mechanisms for ensuring the integrity of the reasoning process. Recently, Chain-of-Thought and Tree-of-Thought prompting methods have made some progress in improving reasoning transparency, but they remain vulnerable to adversarial manipulation at the output layer. To enhance the reasoning reliability of LLMs, a new architectural approach is needed to ensure the integrity of the reasoning process.

Large language models are prone to hallucinations and unreliable reasoning under adversarial prompting, which is particularly concerning in high-stakes applications. Existing safety methods, such as Reinforcement Learning from Human Feedback (RLHF) and output filtering, primarily operate at the behavioral level and lack explicit architectural mechanisms for ensuring the integrity of the reasoning process. Additionally, these methods exhibit significant vulnerabilities when models prioritize user satisfaction over factual accuracy, even in aligned models. The core issue is the lack of non-bypassable architectural constraints to ensure the integrity of the reasoning process.

The Box Maze framework introduces a new architectural approach by decomposing the reasoning process into three explicit layers: memory grounding, structured inference, and boundary enforcement. Memory grounding ensures temporal consistency, preventing retroactive confabulation; structured inference checks causal consistency through mathematical ontology, preventing logical inconsistencies; and boundary enforcement uses mutex constraints to maintain epistemological boundaries, preventing hallucination generation under adversarial prompting. Compared to existing Chain-of-Thought and Tree-of-Thought prompting methods, the Box Maze framework significantly enhances adversarial robustness through middleware-level constraint embedding.

The core of the Box Maze framework lies in its three interlocking loops that constrain the reasoning process at the middleware layer:

• �� Memory Loop (Temporal Anchoring): Each step is timestamped and immutably recorded, preventing retroactive confabulation.

• �� Logic Loop (Structured Inference): Causal consistency checking through mathematical ontology prevents logical inconsistencies.

• �� Heart Anchor (Boundary Enforcement): Mutex constraints ensure epistemological boundaries are maintained, preventing hallucination generation under adversarial prompting.

The framework's design philosophy is to ensure the integrity and consistency of the reasoning process by embedding constraint layers at the middleware level.

The experimental design includes simulation experiments across multiple heterogeneous LLM systems (e.g., DeepSeek-V3, Doubao, Qwen) to test the robustness of the Box Maze framework under adversarial prompting. The experiments involve n=50 adversarial scenarios with progressively increasing difficulty, including forward-logic traps (emotional blackmail), reverse-logic scenarios (temporal confusion), and high-stakes coercion (requiring false admissions to 'save' the user). These experiments evaluate the performance of the Box Maze framework across different scenarios.

The experimental results show that the Box Maze framework significantly reduces boundary failure rates from approximately 40% (baseline RLHF) to below 1% under adversarial prompting. Ablation studies further reveal that the Heart Anchor (mutex constraint layer) is critical for extreme coercion resistance, with its removal leading to immediate vulnerability under emotional manipulation. Additionally, cross-model validation demonstrates the model-agnostic nature of the constraints, with the Box Maze framework showing robustness across different LLM systems.

The Box Maze framework holds potential industrial applications in scenarios requiring high reliability and safety, such as autonomous driving, medical diagnosis, and financial analysis. By embedding constraint layers at the middleware level, the Box Maze framework can significantly enhance the reasoning reliability of systems, reducing the probability of hallucination generation and improving the safety and reliability of these high-stakes applications.

Despite the promising results of the Box Maze framework in simulation experiments, its full middleware implementation (e.g., kernel-level process isolation) is still ongoing and incomplete. Additionally, current validation is based on simulation experiments and has not been statistically validated in real-world applications, which may affect its applicability in practical environments. In some extreme emotional manipulation scenarios, the system may misclassify, requiring further optimization. Future work will include completing the full middleware implementation of the Box Maze framework, conducting large-scale statistical validation, and exploring how to apply the framework across more heterogeneous LLM systems.