ReVision tackles the token cost problem for computer-use agents that observe screenshots over time. Each screenshot encodes into many visual tokens, so trajectories quickly become expensive. The authors train a patch selector that removes redundant visual patches across consecutive screenshots while preserving spatial structure. On Qwen2.5-VL-7B processing 5 history screenshots, ReVision cuts token usage by ~46% and improves success rate by 3%. Importantly, they find that performance keeps improving with more history when redundancy is removed, suggesting that visual history saturation isn't due to limited usefulness but inefficient representations.
Main takeaways:
- Computer-use agents process screenshots that encode into huge numbers of visual tokens, limiting how much history fits in context.
- ReVision trains a patch selector to remove redundant visual patches between consecutive frames while keeping spatial structure intact.
- Reduces tokens by ~46% on average across three benchmarks while improving success rate by 3% (Qwen2.5-VL-7B, 5 history screenshots).
- Performance continues improving with more history when redundancy is removed, challenging the idea that visual history has diminishing returns.
- Suggests commonly observed saturation is due to inefficient token representations, not limited usefulness of past observations.