# Visual Token Routing

Computer-use agents encode each screenshot into thousands of visual tokens, and those tokens pile up into long contexts on long-horizon tasks and in real-time games. VTR trains a vision-language model jointly with a lightweight spatio-temporal router that inspects the same image region in the current and previous screenshots and decides whether that token enters the language model, using an adaptive loss to keep as few tokens as possible without losing action prediction. After only 2,000 training steps, VTR matches the full-resolution model on OSWorld, CUA-World and video-game action prediction while using 2.5–5.8× fewer visual tokens, cutting inference cost by up to 3.3× and speeding prefill by up to 2.9×.

Computer-use agents encode each screenshot into thousands of visual tokens, and those tokens pile up into long contexts on long-horizon tasks and in real-time games. VTR trains a vision-language model jointly with a lightweight spatio-temporal router that inspects the same image region in the current and previous screenshots and decides whether that token enters the language model, using an adaptive loss to keep as few tokens as possible without losing action prediction. After only 2,000 training steps, VTR matches the full-resolution model on OSWorld, CUA-World and video-game action prediction while using 2.5–5.8× fewer visual tokens, cutting inference cost by up to 3.3× and speeding prefill by up to 2.9×.