26 citations · 44 across the 6 of their papers we have counts for
6 papers
WebWorld: A Large-Scale World Model for Web Agent Training
Zikai Xiao, Jianhong Tu, Chuhang Zou +7
Web agents require massive trajectories to generalize, yet real-world training is constrained by network latency, rate limits, and safety risks. We introduce \textbf{WebWorld} seri…
Qwen3-VL Technical Report
Shuai Bai, Yuxuan Cai, Ruizhe Chen +61
We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively…
AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy
Jinghang Shi, Xiaoyu Tang, Yang Huang +4
Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…
GeoGPT-RAG Technical Report
Fei Huang, Fan Wu, Zeqing Zhang +4
GeoGPT is an open large language model system built to advance research in the geosciences. To enhance its domain-specific capabilities, we integrated Retrieval Augmented Generatio…
Long Is More Important Than Difficult for Training Reasoning Models
Si Shen, Fei Huang, Zhixiao Zhao +3
Difficult problems, which often result in long reasoning traces, are widely recognized as key factors for enhancing the performance of reasoning models. However, such high-challeng…
Evaluating Psychological Safety of Large Language Models
Xingxuan Li, Yutong Li, Lin Qiu +2
In this work, we designed unbiased prompts to systematically evaluate the psychological safety of large language models (LLMs). First, we tested five different LLMs by using two pe…