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20242026
most citedChinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models

1 citations · 3 across the 20 of their papers we have counts for

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5 papers · 1 filter

cs.AI2025

QuadSentinel: Sequent Safety for Machine-Checkable Control in Multi-agent Systems

Yiliu Yang, Yilei Jiang, Qunzhong Wang +5

Safety risks arise as large language model-based agents solve complex tasks with tools, multi-step plans, and inter-agent messages. However, deployer-written policies in natural la…

cs.AI2025

AI Deception: Risks, Dynamics, and Controls

Boyuan Chen, Sitong Fang, Jiaming Ji +56

As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an…

cs.AI2025

Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem

Weixun Wang, XiaoXiao Xu, Wanhe An +86

Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its impo…

cs.AI2025

Beyond Safe Answers: A Benchmark for Evaluating True Risk Awareness in Large Reasoning Models

Baihui Zheng, Boren Zheng, Kerui Cao +9

Despite the remarkable proficiency of \textit{Large Reasoning Models} (LRMs) in handling complex reasoning tasks, their reliability in safety-critical scenarios remains uncertain.…

cs.AI2025★ 1 cited

Equilibrate RLHF: Towards Balancing Helpfulness-Safety Trade-off in Large Language Models

Yingshui Tan, Yilei Jiang, Yanshi Li +6

Fine-tuning large language models (LLMs) based on human preferences, commonly achieved through reinforcement learning from human feedback (RLHF), has been effective in improving th…