collaborators

10 papers

cs.AI2026

Agent-Orchestration in Autonomous Chip Design

Linyang Li

Recent developments in large language models (LLMs) and tool-using agents encourage people to explore the potential of using agents in chip design. The core question is what kind o…

cs.AI2026

MatrAIx: Simulating the World with 8.3 Billion Persona Agents

Xiaomin Li, Yuexing Hao, Jianheng Hou +90

Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and inter…

cs.CL2026

CurveShift: Is Agent Progress Scalar? Separating Level from Shape

Hanwen Xing, Pengyun Wang, BingXu Meng +8

Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summa…

cs.AI2026

OpenThoughts-Agent: Data Recipes for Agentic Models

Negin Raoof, Richard Zhuang, Marianna Nezhurina +47

Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts…

cs.AI2026

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

Xiangyi Li, Yimin Liu, Wenbo Chen +75

Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to m…

cs.AI2026

Agents' Last Exam

Yiyou Sun, Xinyang Han, Weichen Zhang +306

Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…