most citedSkillNet: Create, Evaluate, and Connect AI Skills

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence

Mengru Wang, Junfeng Fang, Shuofei Qiao +17

AI models are increasingly used in scientific discovery and human decision-making. Yet how AI models work and what risks they pose remain poorly understood. As AI development becom…

cs.AI2026

MobileMem: Learning from a Year of Mobile Experiences

Xinle Deng, Yida Xue, Xiangyuan Ru +16

The next generation of AI agents is increasingly moving beyond systems that answer isolated questions toward persistent personal assistants that can understand, remember, and conti…

cs.CL2026

LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories

Baochang Ren, Xinjie Liu, Xi Chen +15

Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read lit…

cs.MM2026

OceanPile: A Large-Scale Multimodal Ocean Corpus for Foundation Models

Yida Xue, Ningyu Zhang, Tingwei Wu +5

The vast and underexplored ocean plays a critical role in regulating global climate and supporting marine biodiversity, yet artificial intelligence has so far delivered limited imp…

cs.AI2026★ 1 cited

SkillNet: Create, Evaluate, and Connect AI Skills

Yuan Liang, Ruobin Zhong, Haoming Xu +47

Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic accumulation and transfer of skills. Wi…

cs.CL2025

OceanGym: A Benchmark Environment for Underwater Embodied Agents

Yida Xue, Mingjun Mao, Xiangyuan Ru +9

We introduce OceanGym, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. Unlike t…

cs.AI2025

Logical Structure as Knowledge: Enhancing LLM Reasoning via Structured Logical Knowledge Density Estimation

Zhen Bi, Zhenlin Hu, Xueshu Chen +7

The reasoning capabilities of Large Language Models (LLMs) are increasingly attributed to training data quality rather than mere parameter scaling. However, existing data-centric p…