Papers by Gengze Zhou

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Challenge:	Multimodal Large Language Models have demonstrated remarkable capabilities across vision-language tasks, but their performance as embodied agents needs further exploration.
Approach:	They propose a framework to evaluate multimodal large language models as zero-shot agents . they find that enhancing prevalent agents with Chain-of-Thought reasoning and self-reflection leads to an unexpected performance decrease.
Outcome:	The proposed framework enables comparisons and component-level ablations across diverse MLLM architectures, agent designs, and navigation tasks.