arXiv:2606.17372v1 Announce Type: new 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 while directly comparing their prompting styles. We replicate the finding that models can coordinate efficient referring expressions when explicitly prompted to do so, suggesting that other task differences are not responsible for divergent results. However, we also find that the same models fail to infer the need for commu
This research addresses divergent findings from recent studies on Large Vision-Language Models (LVLMs) in referential communication, indicating an active and evolving understanding of how these models perform communication tasks.
Understanding the difference between implicit and explicit prompting for LVLMs is crucial for efficiently developing more capable and robust AI agents that can truly understand and generate nuanced communication.
This research highlights that explicit prompting is currently necessary for LVLMs to achieve efficient referential communication, suggesting that model capabilities for inferring communication needs are not yet mature.
- · AI researchers
- · Developers of LVLM applications
- · Companies investing in explainable AI
- · Developers relying solely on implicit prompting for complex LVLM tasks
Further research will focus on improving LVLMs' ability to infer communication needs without explicit prompting, leading to more autonomous AI agents.
Improved implicit communication capabilities in LVLMs could significantly reduce the complexity and cost of developing advanced AI systems.
As LVLMs become more adept at inferring context, they may enable more natural and intuitive human-computer interfaces, potentially accelerating the development of sophisticated AI agents.
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