arXiv: cs.AI preprintsInternational6 November 2026
Can Linguistic Reasoning Vectors Enhance Multimodal Reasoning Ability?
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arXiv:2609.31140v2 Announce Type: replace Abstract: Most Vision-Language Models (VLMs) are built by extending pretrained Large Language Models (LLMs) with visual modules and multimodal alignment. However, this multimodal scaling often degrades the language-side reasoning ability originally encoded in the base LLM. While the base LLM retains usable reasoning after scaling, the aligned VLM itself cannot reliably access this ability. Therefore, recovering the degraded reasoning capability in VLMs would benefit more from seeking help from the base LLM than from the VLM alone. Motivated by this, we
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