Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models
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 per
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- LinkedLinked via arxiv author · 85%Zhiyu Chen →
“Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Ting Lei →
“Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Yaoyi Li →
“Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Jia Cai →
“Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Zhecen Wu →
“Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Dongyang Liu →
“Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models”