activity
20222026
most citedHumanity's Last Exam

23 citations · 44 across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO2026

DiagGen: Agentic Generation of Deformable Assets with Sim-based Diagnostics for Robotic Simulation

Guanxiong Chen, Yiduo Qu, Qianjun Xia +12

While simulation-ready deformable assets are essential for in-silico robotic manipulation tasks, existing generation frameworks typically assess physical plausibility after generat…

cs.AI2026

Apodex Discovery: Reality Benchmarks and Environments for Evaluating and Building Discoverative Artificial Intelligence

Brian Wang, Bin Feng, Xiaoman Pan +26

Apollo did not reach the Moon merely because its engineers could solve difficult equations. It succeeded by turning a distant ambition into a mission architecture of explicit objec…

cs.CV2026

A multi-agent system for spine MRI report generation from multi-sequence imaging

Zhiping Xiao, Junwei Yang, Gongbo Sun +12

Spinal pathology is a leading cause of pain and disability worldwide. Spine MRI is central to clinical evaluation, yet its interpretation remains complex and time-consuming, requir…

cs.LG2025★ 23 cited

Humanity's Last Exam

Long Phan, Alice Gatti, Ziwen Han +1168

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…

cs.CL2024★ 15 cited

A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning

Ye Yuan, Chengwu Liu, Jingyang Yuan +3

Retrieval-augmented generation (RAG) is a framework enabling large language models (LLMs) to enhance their accuracy and reduce hallucinations by integrating external knowledge base…

cs.LG2022

GLCC: A General Framework for Graph-Level Clustering

Wei Ju, Yiyang Gu, Binqi Chen +5

This paper studies the problem of graph-level clustering, which is a novel yet challenging task. This problem is critical in a variety of real-world applications such as protein cl…

cs.CV2022★ 6 cited

End-to-End Rubbing Restoration Using Generative Adversarial Networks

Gongbo Sun, Zijie Zheng, Ming Zhang

Rubbing restorations are significant for preserving world cultural history. In this paper, we propose the RubbingGAN model for restoring incomplete rubbing characters. Specifically…