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About this paper
Guibin Zhang, Yaohui Ling, Fanci Meng, Kun Wang, Shuicheng Yan
Mem-W offers a latent-memory-native framework for GUI agents, unifying short-horizon working memory and long-horizon experiential memory into a continuous latent space. This approach enhances agents' ability to distill and integrate task-relevant historical evidence, improving performance by up to +30.00 points on MMINA and +20.58 on Multimodal-Mind2Web across diverse web and mobile GUI benchmarks.
Key insights
- A unified latent memory space, learned end-to-end, can effectively replace manually engineered memory hierarchies, resolving representational mismatch and architectural rigidity in GUI agents.
- Treating memory as an intrinsic part of the agent's continuous context allows task-relevant memory organization to emerge from end-to-end training, enhancing adaptability and scalability.
- Outcome-aware supervision combined with self-distillation enables the latent compressor to effectively distill and filter memory, prioritizing procedural evidence tied to task success for improved performance.
Summary by alphaXiv.
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