MAPS: Preserving Vision-Language Representations via Module-Wise Proximity Scheduling for Better Vision-Language-Action Generalization
Published:Nov 25, 2025 03:39
•1 min read
•ArXiv
Analysis
This article introduces MAPS, a method for improving vision-language-action generalization. The core idea revolves around preserving vision-language representations using a module-wise proximity scheduling strategy. The paper likely details the specific scheduling mechanism and evaluates its performance on relevant benchmarks. The focus is on improving the ability of AI models to understand and act upon visual and linguistic information.
Key Takeaways
Reference
“The article likely discusses the specific scheduling mechanism and its impact on generalization performance.”