Papers by Zhaoyang Xu

3 papers
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains (2025.findings-naacl)

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Challenge: Existing benchmarks focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes.
Approach: They propose a Massive Multitask Agent Understanding benchmark that evaluates LLMs across five domains and offline tasks.
Outcome: The Massive Multitask Agent Understanding (MMAU) benchmark evaluates models across five domains including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics.
OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)

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Challenge: Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning.
Approach: They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation.
Outcome: Experimental results show that OS-Symphony delivers substantial performance gains across model scales.
VISIAR: Empower MLLM for Visual Story Ideation (2025.findings-acl)

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Challenge: Existing literature on visual storytelling has not explored the ideation process fully.
Approach: They propose a visual story ideation task that automates the selection and arrangement of visual assets into coherent sequences that convey expressive storylines.
Outcome: The proposed framework surpasses baseline by 33.5% and 18.5%, respectively, on three metrics.

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