Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models

2026-08-20

AI Summary

研究通过引入RuleMaze基准和Disentangled Multimodal Planning方法，解决多模态大语言模型在遵守自然语言规则下的视觉空间规划问题。

Abstract

Multimodal large language models (MLLMs) combine linguistic reasoning with visual perception, yet their ability to perform visual spatial planning under explicit or previously unseen rule constraints remains underexplored. This setting requires models to jointly understand spatial layouts, interpret natural-language rules, and plan valid actions accordingly. To address this gap, we introduce RuleMaze, a controllable benchmark in which MLLMs must navigate mazes while obeying natural-language rules of varying complexity. RuleMaze isolates rule-compliant spatial planning by requiring accurate perception, rule interpretation, and constrained action planning. To enable scalable and systematic rule construction, we propose Language-Logic-Function Hybridization, which automatically generates natural-language rules and translates them into logical representations and executable validators, eliminating manual rule engineering. To improve rule following and generalization, we introduce Disentangled Multimodal Planning (DMP), which separates perception, execution, and rule verification through interpretable reasoning primitives. By disentangling these components, DMP facilitates systematic generalization to more complex and previously unseen rules, while providing transparent intermediate planning traces. Experiments demonstrate that DMP substantially improves rule compliance and planning success compared to end-to-end textual planning baselines. Overall, RuleMaze establishes a principled benchmark for studying grounded and interpretable rule-based spatial planning in MLLMs. Code is available at https://github.com/oceanflowlab/RuleMaze.

Problem

• Multimodal Large Language Models
• Visual Spatial Planning
• Rule Constraints
• Natural-Language Rules
• Action Planning

Innovation

• RuleMaze
• Language-Logic-Function Hybridization
• Disentangled Multimodal Planning
• rule-compliant spatial planning