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This paper presents the first systematic empirical study of using visual repository representations to enhance LLM-based coding agents, showing that integrating visual graphs as a supplementary modality reduces token consumption by up to 26% while maintaining or improving issue-resolution accuracy.
This paper proposes a causal framework for probing internal visual representations in Multimodal Large Language Models, revealing differences in how entities and abstract concepts are encoded. The study highlights that increasing model depth is crucial for encoding abstract concepts and uncovers a disconnect between perception and reasoning in current MLLMs.