Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models: RuleMaze Benchmark and Disentangled Planning
Score
79首次系统性地提出MLLM规则约束下的空间规划基准与解耦方法，填补现有评测空白。
方法设计严谨，规则生成与验证器自动化程度高，实验对比充分，论证清晰。
开源代码与可扩展基准为后续研究提供了直接可用的工具和参考实现。
切合当前多模态模型可控性与可解释性研究热点，具有较强时效性。
论文来自arXiv AI/CL/LG板块并附带开源代码，来源可靠且可验证。
Base 88 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
The paper is highly original, introducing a new benchmark and decoupling method with solid experiments, clearly advancing research on spatial planning in MLLMs.
AI commentary
「This paper is worth attention: it pushes MLLM evaluation from visual QA to rule-constrained spatial planning, targeting a key weakness of current large models. For researchers in embodied AI, agent planning, or interpretability, RuleMaze and DMP provide strong baselines to compare against.」
Quick overview
Multimodal large language models combine linguistic reasoning with visual perception, yet their ability to perform visual spatial planning under explicit or unseen rule constraints remains underexplored. To address this gap, the authors introduce RuleMaze, a controllable benchmark requiring models to navigate mazes while obeying natural-language rules of varying complexity. They also propose Language-Logic-Function Hybridization for automatic rule generation, and Disentangled Multimodal Planning (DMP) that separates perception, execution, and rule verification. Experiments show DMP substantially improves rule compliance and planning success over end-to-end textual baselines, offering transparent intermediate traces for grounded and interpretable spatial planning.
- Introduces RuleMaze, a benchmark for evaluating MLLMs' visual spatial planning under natural-language rule constraints.
- Proposes Language-Logic-Function Hybridization to automatically generate rules and convert them into executable logical validators.
- Presents Disentangled Multimodal Planning (DMP) that decouples perception, execution, and rule verification for better generalization and interpretability.
- Experiments show DMP substantially outperforms end-to-end textual planning baselines in rule compliance and planning success.
Full text
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