1 citations · 3 across the 5 of their papers we have counts for
5 papers
Efficient Agent Training for Computer Use
Yanheng He, Jiahe Jin, Pengfei Liu
Scaling up high-quality trajectory data has long been a critical bottleneck for developing human-like computer use agents. We introduce PC Agent-E, an efficient agent training fram…
Generative AI Act II: Test Time Scaling Drives Cognition Engineering
Shijie Xia, Yiwei Qin, Xuefeng Li +11
The first generation of Large Language Models - what might be called "Act I" of generative AI (2020-2023) - achieved remarkable success through massive parameter and data scaling,…
Revisiting 3D LLM Benchmarks: Are We Really Testing 3D Capabilities?
Jiahe Jin, Yanheng He, Mingyan Yang
In this work, we identify the "2D-Cheating" problem in 3D LLM evaluation, where these tasks might be easily solved by VLMs with rendered images of point clouds, exposing ineffectiv…
PC Agent: While You Sleep, AI Works -- A Cognitive Journey into Digital World
Yanheng He, Jiahe Jin, Shijie Xia +5
Imagine a world where AI can handle your work while you sleep - organizing your research materials, drafting a report, or creating a presentation you need for tomorrow. However, wh…
BeHonest: Benchmarking Honesty in Large Language Models
Steffi Chern, Zhulin Hu, Yuqing Yang +5
Previous works on Large Language Models (LLMs) have mainly focused on evaluating their helpfulness or harmlessness. However, honesty, another crucial alignment criterion, has recei…