GUI-KV is a plug‑and‑play key‑value cache compression method designed for graphical user interface agents that use vision‑language models. It exploits spatial saliency and temporal redundancy to prune cache entries, achieving near‑full‑cache accuracy while using modest memory budgets. Experiments on standard GUI agent benchmarks show significant reductions in decoding FLOPs and improvements in step accuracy compared to existing compression baselines.
By Kung-Hsiang Huang, Haoyi Qiu, Yutong Dai, Caiming Xiong, Chien-Sheng Wu
arXiv:2610.14228v1 Announce Type: cross
Abstract: Pixel-space world models have recently emerged as a promising approach for GUI agents, predicting future screens prior to execution and subsequently...
By Seungjun Yu, Hojun Choi, Seojeong Park, Jaeyo Shin, Hyunjung Shim
arXiv:2610.01215v1 Announce Type: new
Abstract: GUI agents require high-quality interaction trajectories to learn how software environments respond to actions, maintain state, and support multi-step...
By Cheng Yang, Yifan Wu, Yutao Huang, Zhaohua Zhang, Beiduo Chen, Muxi Chen, Chenchen Zhao, Hexuan Deng, Haolin Yang, Geyuan Zhu, Sa Zhu, Jianhuan Zhuo, Qiuyong Xiao, Jianhao Ruan, Yiran Peng, Jiayi Zhang, Tian Ye, Xinlei Yu, Tianwen Jiang, Jihong Zhang, Yuyu Luo
GUI agents require high-quality interaction trajectories to learn how software environments respond to actions, maintain state, and support multi-step workflows. However, the diversity of available tr...
The paper introduces IG‑VLA, a vision‑language‑action framework that learns to imagine task‑relevant future scene evolution in a latent spatiotemporal space, guiding action prediction without generating full pixel‑level videos. It further compresses this future reasoning into a compact Scene Gist Token via Scene Gist Memory, enabling efficient inference. Experiments on LIBERO, LIBERO‑Plus, and VLABench show that IG‑VLA improves success rates by nearly 6% and speeds up inference up to 6.38× compared to strong baselines.
By Shenglan Li, Zhendong Mi, Hengyi Zhu, Jingwu Luo, Chun Kit Chan, Geng Yuan, Yanzhi Wang, Pu Zhao, Shaoyi Huang
ECHOES is a method for long‑horizon GUI agents that compresses visual history into a textual state using a Compression‑Think‑Action format. It employs a privileged teacher model that provides token‑level on‑policy distillation to guide the student’s textual rollouts, while a Perception‑Memory Fidelity Gap‑guided framework balances distillation with task reward optimization. The approach is evaluated on four GUI benchmarks and a new ECHOESBMK diagnostic benchmark, showing improved task performance, memory fidelity, and a favorable trade‑off between performance and inference cost, with positive zero‑shot transfer to unseen planning tasks.
By Tengda Guo (Tsinghua University), Jiawang Hu (StepFun), Yifei Tao (Nanyang Technological University), Yin Wang (StepFun), Haotian Hu (Zhejiang Leapmotor Technology Co., Ltd), Fanyi Wang (Honor), Liujuan Tang (StepFun)