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Day 2 of VLX, VLX-Seek: improving VLM fine-grained perception via region reference instead of coordinate generation! VLMs are good at understanding what is in an image, but still struggle to pinpoint where it is. Coordinate generation is fragile: long numeric outputs, formatting errors, missed objects, and hallucinated boxes. VLX-Seek takes a different path: region reference instead of coordinate generation. It retrieves candidate regions, turns them into language-addressable region tokens, and lets the model select <region_i> instead of generating [x1, y1, x2, y2]. From detection and referring expressions to counting, OCR, and embodied interaction, VLX-Seek pushes VLMs from “seeing the image” toward “grounding objects in space.” Github：https://t.co/JUl9jPttwg huggingface：https://t.co/rrig30Dn8e #VLX #VLXModel #VLXSeries #StreamingMultimodal #OnDeviceAI #PhysicalAI #EmbodiedAI #EdgeAI #VisionLanguageModel #AI