1 citations · 1 across the 9 of their papers we have counts for
9 papers
StressWeb: A Diagnostic Benchmark for Web Agent Robustness under Realistic Interaction Variability
Haoyue Bai, Dong Wang, Long Chen +5
Large language model-based web agents have demonstrated strong performance on realistic web interaction tasks. However, existing evaluations are predominantly conducted under relat…
V2P: Visual Attention Calibration for GUI Grounding via Background Suppression and Center Peaking
Jikai Chen, Long Chen, Dong Wang +6
Precise localization of GUI elements is crucial for the development of GUI agents. Traditional methods rely on bounding box or center-point regression, neglecting spatial interacti…
From Failure to Mastery: Generating Hard Samples for Tool-use Agents
Bingguang Hao, Zengzhuang Xu, Yuntao Wen +11
The advancement of LLM agents with tool-use capabilities requires diverse and complex training corpora. Existing data generation methods, which predominantly follow a paradigm of r…
FunReason-MT Technical Report: Advanced Data Synthesis Solution for Real-world Multi-Turn Tool-use
Zengzhuang Xu, Bingguang Hao, Zechuan Wang +14
Function calling (FC) empowers large language models (LLMs) and autonomous agents to interface with external tools, a critical capability for solving complex, real-world problems.…
V2P: Visual Attention Calibration for GUI Grounding via Background Suppression and Center Peaking
Jikai Chen, Long Chen, Dong Wang +6
Precise localization of GUI elements is crucial for the development of GUI agents. Traditional methods rely on bounding box or center-point regression, neglecting spatial interacti…
Reasoning through Exploration: A Reinforcement Learning Framework for Robust Function Calling
Bingguang Hao, Zengzhuang Xu, Maolin Wang +9
The effective training of Large Language Models (LLMs) for function calling faces a critical challenge: balancing exploration of complex reasoning paths with stable policy optimiza…
FunReason: Enhancing Large Language Models' Function Calling via Self-Refinement Multiscale Loss and Automated Data Refinement
Bingguang Hao, ZengZhuang Xu, Maolin Wang +9
The integration of large language models (LLMs) with function calling has emerged as a crucial capability for enhancing their practical utility in real-world applications. However,…
E-Bench: Towards Evaluating the Ease-of-Use of Large Language Models
Zhenyu Zhang, Bingguang Hao, Jinpeng Li +2
Most large language models (LLMs) are sensitive to prompts, and another synonymous expression or a typo may lead to unexpected results for the model. Composing an optimal prompt fo…
From Global to Local: Multi-scale Out-of-distribution Detection
Ji Zhang, Lianli Gao, Bingguang Hao +3
Out-of-distribution (OOD) detection aims to detect "unknown" data whose labels have not been seen during the in-distribution (ID) training process. Recent progress in representatio…