arXiv:2601.19792v4 Announce Type: replace 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 common ground. We present a referential communication experiment with a factorial design involving director-matcher pairs (human-human, human-AI, AI-human, and AI-AI) that interact with multiple turns in repeated rounds to match pictures of objects not associated with any obvious lexicalized labels. We show that LVLMs cannot in
The proliferation of advanced LVLMs and the increasing drive for AI integration into daily human workflows necessitate understanding their limitations in collaborative common ground establishment.
This research highlights a fundamental barrier to seamless human-AI collaboration, suggesting that current AI approaches may struggle with nuanced human intent, which is critical for agentic systems.
The focus shifts from raw generative capability to the quality of human-AI interaction, particularly common ground formation, as a key differentiator for effective AI applications.
- · Cognitive AI research
- · Human-centered AI design
- · AI agent developers focusing on intent modeling
- · AI agents lacking sophisticated common ground models
- · Purely data-driven generative AI approaches
Further research and development will be directed towards improving AI's ability to model and establish common ground with human users.
This could lead to a divergence in AI development, with some systems prioritizing 'understanding' human intent over raw output generation speed or scale.
Long-term, successfully addressing this challenge could unlock new paradigms for human-AI co-creation and problem-solving, but failure could lead to widespread frustration and rejection of 'dumb' agents.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL