2 citations · 3 across the 8 of their papers we have counts for
8 papers
OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong +33
Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of f…
Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
Song-Lin Lv, Weiming Wu, Rui Zhu +2
While Large Language Model (LLM) agents demonstrate proficiency in static benchmarks, their deployment in real-world scenarios is hindered by the dynamic nature of user queries, to…
A Progressive Visual-Logic-Aligned Framework for Ride-Hailing Adjudication
Weiming Wu, Zi-Jian Cheng, Jie Meng +5
The efficient adjudication of responsibility disputes is pivotal for maintaining marketplace fairness. However, the exponential surge in ride-hailing volume renders manual review i…
MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs
Ziqiao Shang, Ling-Yue Ge, Zian Xu +10
Systematically evaluating Multimodal Large Language Models (MLLMs) is essential for advancing Artificial General Intelligence (AGI). Yet existing benchmarks remain inadequate for r…
On Path to Multimodal Generalist: General-Level and General-Bench
Hao Fei, Yuan Zhou, Juncheng Li +29
The Multimodal Large Language Model (MLLM) is currently experiencing rapid growth, driven by the advanced capabilities of LLMs. Unlike earlier specialists, existing MLLMs are evolv…
NeSyGeo: A Neuro-Symbolic Framework for Multimodal Geometric Reasoning Data Generation
Weiming Wu, Jin Ye, Zi-kang Wang +3
Obtaining large-scale, high-quality reasoning data is crucial for improving the geometric reasoning capabilities of multi-modal large language models (MLLMs). However, existing dat…
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student Architectures
Kuluhan Binici, Weiming Wu, Tulika Mitra
Knowledge distillation (KD) is a model compression method that entails training a compact student model to emulate the performance of a more complex teacher model. However, the arc…
Instance-aware Model Ensemble With Distillation For Unsupervised Domain Adaptation
Weimin Wu, Jiayuan Fan, Tao Chen +3
The linear ensemble based strategy, i.e., averaging ensemble, has been proposed to improve the performance in unsupervised domain adaptation tasks. However, a typical UDA task is u…