A new method called TRACE (Trajectory-robust Admission with Evidence Ordering) addresses the growing inference latency and memory costs of GUI agents that accumulate high-resolution screenshots as their trajectories unfold. The approach targets training-free visual token pruning, but the work notes that cache reuse imposes a fundamental constraint: once tokens are discarded, the corresponding visual evidence cannot be recovered.
GUI agents accumulate high-resolution screenshots as the trajectory unfolds, increasing inference latency and memory usage. Training-free visual token pruning can reduce this cost, but cache reuse introduces a fundamental constraint. Once tokens are discarded, the corresponding visual evidence canno
EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.