VISTA: A Visual Harness for Reasoning in an Interactive World
Qiushi Han, Keya Hu, Linlu Qiu, Cathy Wu, Kaiming He
arXiv:2610.02200v1Today’s standout paper is VISTA, a visual harness for reasoning in interactive worlds. The problem it tackles is that strong multimodal models often underperform in games, puzzles, and other environments not because they lack reasoning, but because they can’t keep track of what they’ve seen over long horizons. VISTA gives the model a better interface: it can directly observe the world through images, store a lossless visual memory of past observations, and retrieve and reorganize those observations as it plans its next move. In other words, instead of forcing the model to reason from a thin text summary, it lets the model think with the full visual record. That matters because it unlocks existing model capability without retraining the model itself. On ARC-AGI-3, the system dramatically improves performance and uses far fewer actions than humans. More broadly, this suggests that better scaffolding, not just bigger models, may be key to building capable visual agents.
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