arXiv:2605.21796v1 Announce Type: cross Abstract: Grounding language in the physical world requires AI systems to interpret references that emerge dynamically during conversation. While current vision-language models (VLMs) excel at static image tasks, they struggle to resolve ambiguous expressions in spontaneous, multi-turn dialogue. We address this gap by introducing (1) a benchmark for referential communication in dynamic 3D environments, built from 6.7 hours of egocentric VR interaction with synchronized speech, motion, gaze, and 3D scene geometry, and (2) a two-stage grounding pipeline th
The increasing sophistication of AI models and the demand for more robust human-AI interaction are driving the creation of benchmarks like MM-Conv, moving beyond static image tasks to dynamic, multimodal environments.
This development is crucial for advancing AI's ability to understand and interact with the physical world, which is a prerequisite for a wide range of autonomous systems and agents.
Current vision-language models will need to evolve to efficiently process and ground ambiguous expressions in real-time within complex 3D environments, leading to more capable and context-aware AI.
- · AI researchers
- · Robotics companies
- · VR/AR developers
- · Generative AI platforms
- · Developers of static vision-language models
- · AI systems lacking multimodal grounding capabilities
Improved multimodal AI models capable of more nuanced understanding of human instructions in dynamic environments.
Accelerated development of AI agents and humanoid robots that can effectively navigate and interact with the real world.
Enhanced human-robot collaboration across various sectors, from manufacturing and logistics to healthcare and personal assistance.
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