Papers by Hanzhang Yuan

2 papers
FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems (2026.findings-acl)

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Challenge: a lack of benchmarks capture real-world, cross-platform heterogeneity in GUI training . traditional methods to train GUI agents rely on centralized data collection and manual labeling .
Approach: They propose a benchmark for developing and evaluating federated GUI agents across mobile, web and desktop platforms.
Outcome: The proposed benchmarks show that cross-platform collaboration improves performance and identify platform and OS as the most influential factors.
Reducing Token Redundancy in LVLMs: A Systematic Review of Token Pruning Methods (2026.acl-long)

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Challenge: Large Vision-Language Models (LVLMs) excel at visual understanding but face severe computational bottlenecks when processing high-resolution images and long videos due to massive visual token counts.
Approach: They propose a taxonomy categorizing methods into vision-side, LLM-side and hybrid paradigms and analyze token selection mechanisms and pruning strategy.
Outcome: The proposed method selectively removes less informative tokens while maintaining performance.

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