VLX-Seek-1.5-10B: A 10B Vision-Language Model for Embodied AI
The open-source VLX-Seek-1.5-10B vision-language model has been released, designed to improve visual grounding in embodied AI. Instead of generating raw coordinates, it reformulates localization as region retrieval and reference by converting candidate regions into language-addressable tokens. This approach aligns localization with the reasoning and selection strengths of language models, making it highly suitable for edge-side robotics, drones, and surveillance systems. By avoiding fragile coordinate-string generation, it provides more stable region-level anchors for physical AI agents. The model features faster Object Proposal Network (OPN) generation, linear attention layers for efficient inference, and explicit absent-target rejection to reduce hallucinations. However, it relies heavily on the quality of candidate region proposals and requires a post-processing pipeline to map tokens back to image coordinates.
## BACKGROUND
Visual grounding is the process of linking textual descriptions to specific regions or objects within an image. Embodied AI refers to artificial intelligence systems, like robots or drones, that interact physically with their environment, requiring precise spatial reasoning and real-time visual perception to perform tasks.