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cs.CL2026
OpenSkillRisk: Benchmarking Agent Safety When Using Real-World Risky Third-Party Skills
Qiyuan Liu, Tingfeng Hui, Kun Zhan +2
LLM-based agents leverage third-party skills to extend their capabilities in open-world scenarios. However, third-party skills can introduce extra security vulnerabilities, as seem…
cs.CL2026
Beyond Ideal Instruction: A Comprehensive Framework for Evaluating LLMs in Realistic Interactions
Xuan Yang, Hao Xu, Tingfeng Hui +4
Despite great advances in tool-use capabilities of large language models (LLMs), existing evaluation benchmarks struggle to fully align with real-world scenarios. Such benchmarks m…
cs.CL2026
LexInstructEval: Lexical Instruction Following Evaluation for Large Language Models
Huimin Ren, Yan Liang, Baiqiao Su +4
The ability of Large Language Models (LLMs) to precisely follow complex and fine-grained lexical instructions is a cornerstone of their utility and controllability. However, evalua…