Papers by Mengying 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.
Cross-Document Cross-Lingual NLI via RST-Enhanced Graph Fusion and Interpretability Prediction (2025.emnlp-main)

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Challenge: Despite the development of many subdirections, Cross-Document Cross-Lingual NLI remains largely unexplored.
Approach: They propose a novel paradigm that extends traditional NLI capabilities to multi-document, multilingual scenarios by integrating RST-enhanced graph fusion with interpretability-aware prediction.
Outcome: The proposed method improves on existing models and document-level NLI to multi-document, multilingual scenarios.

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