近期关于大型视觉语言模型（LVLMs）在指称性交流方面的两项研究，在它们协调有效指称表达的能力上得出了相互矛盾的结果。Jones等人的一篇论文表明，当明确提示时，LVLMs可以有效地协调，但无法从隐式提示中推断出这种需求。另一篇由Zeng等人撰写的论文指出，LVLMs在指称表达的交互式生成和解析方面存在困难，这凸显了在构建对人类协作至关重要的共同基础方面存在缺陷。两项研究都利用了指称性交流实验来探讨这些差异。 AI
排序理由 两篇在arXiv上发表的学术论文，详细介绍了对LVLM通信能力的研究。
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arXiv:2606.17372v1 Announce Type: cross Abstract: Two recent studies (Jones et al. (2026); Zeng et al. (2026)) reach apparently contradictory conclusions about whether LVLMs can coordinate on efficient referring expressions. We control for task differences between the studies whi…
arXiv:2601.19792v4 Announce Type: replace-cross Abstract: For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical. But this ability to collaborate remains limited by a critical deficit: an inability to model com…
Two recent studies (Jones et al. (2026); Zeng et al. (2026)) reach apparently contradictory conclusions about whether LVLMs can coordinate on efficient referring expressions. We control for task differences between the studies while directly comparing their prompting styles. We r…