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20232026
most citedAre We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

9 citations · 31 across the 26 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

VLA-GAUGE: Systematically Evaluating Generalization and Robustness in Vision-Language-Action Models

Jun Xiao, Rui Ye, Dong Cao +3

Vision-Language-Action models (VLAs) have achieved high success rates on familiar manipulation tasks, yet it remains unclear whether such scores reflect genuine zero-shot generaliz…

cs.HC2026

Learning behavior accounts for background-related advantage in AI-assisted education

Jingwei Yi, Yueqi Xie, Jiyan He +7

Generative AI has been found, and will likely be found increasingly, useful in education. However, existing AI-for-education studies provide inconsistent evidence on its average ef…

cs.AI2026

LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents

Yijun Lu, Rui Ye, Yuwen Du +3

Long-horizon search agents must manage a rapidly growing working context as they reason, call tools, and observe information. Naively accumulating all intermediate content can over…

cs.AI2026

OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories

Yuwen Du, Rui Ye, Shuo Tang +4

Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The t…

cs.AI2026

EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

Xinyu Zhu, Yuzhu Cai, Zexi Liu +20

The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. However, common agent infrastructure is repeatedly rebuilt acr…