MLLMs as Navigation Agents: A Diagnostic Framework
Published:Dec 31, 2025 13:21
•1 min read
•ArXiv
Analysis
This paper introduces VLN-MME, a framework to evaluate Multimodal Large Language Models (MLLMs) as embodied agents in Vision-and-Language Navigation (VLN) tasks. It's significant because it provides a standardized benchmark for assessing MLLMs' capabilities in multi-round dialogue, spatial reasoning, and sequential action prediction, areas where their performance is less explored. The modular design allows for easy comparison and ablation studies across different MLLM architectures and agent designs. The finding that Chain-of-Thought reasoning and self-reflection can decrease performance highlights a critical limitation in MLLMs' context awareness and 3D spatial reasoning within embodied navigation.
Key Takeaways
- •VLN-MME provides a standardized benchmark for evaluating MLLMs in embodied navigation.
- •The framework allows for modular design and easy comparison of different MLLM architectures.
- •CoT and self-reflection can negatively impact MLLM performance in navigation, highlighting limitations in context awareness and spatial reasoning.
Reference
“Enhancing the baseline agent with Chain-of-Thought (CoT) reasoning and self-reflection leads to an unexpected performance decrease, suggesting MLLMs exhibit poor context awareness in embodied navigation tasks.”