most citedA Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?

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cs.CL20261 cited

A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?

Ada Chen, Yongjiang Wu, Junyuan Zhang +6

Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emulate human-like operations in graphical…

cs.CL2026

AlphaEval: Evaluating Agents in Production

Pengrui Lu, Bingyu Xu, Wenjun Zhang +24

The rapid deployment of AI agents in commercial settings has outpaced the development of evaluation methodologies that reflect production realities. Existing benchmarks measure age…

cs.CL2026

LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models

Wei Zhang, Lintong Du, Yuanhe Zhang +4

Despite the strong performance of Large Language Models (LLMs) on complex instruction-following tasks, precise control of output length remains a persistent challenge. Existing met…

cs.CL2025

Jailbreaking Large Language Diffusion Models: Revealing Hidden Safety Flaws in Diffusion-Based Text Generation

Yuanhe Zhang, Fangzhou Xie, Zhenhong Zhou +4

Large Language Diffusion Models (LLDMs) exhibit comparable performance to LLMs while offering distinct advantages in inference speed and mathematical reasoning tasks.The precise an…

cs.CL2025

CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models

Zhenhong Zhou, Zherui Li, Jie Zhang +4

Large Language Model-based Multi-Agent Systems (LLM-MASs) have demonstrated remarkable real-world capabilities, effectively collaborating to complete complex tasks. While these sys…