5 papers · 1 filter
Do LLMs Know Their Vulnerable Scenarios?
Ziheng Peng, Huiqi Deng, Haoran Jing +5
Safety-aligned large language models are trained to refuse harmful requests, yet embedding the same requests in particular scenarios can bypass their safeguards. Existing red-teami…
SkillEval: Decomposing Agent Skill Quality into Interpretable Signals
Jiahui Han, Qinuo Li, Ziheng Peng +6
Agent skills provide reusable knowledge that helps agents solve specialized tasks. As their use expands, evaluating skill quality becomes increasingly important. Existing evaluatio…
Self-Improving Large Language Models via Progressive Experience Evolution
Shijie Ren, Xiting Wang, Meng Li +8
Large language models (LLMs) capable of self-improvement require not only effective policy optimization, but also a principled mechanism for transforming transient interaction expe…
To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents
Wei Shi, Ziheng Peng, Sihang Li +4
LLM agents exhibit a consistent tendency to over-call, invoking tools even in situations where none is needed. On the When2Call benchmark, six models from three families show high…
Target Concept Tuning Improves Extreme Weather Forecasting
Shijie Ren, Xinyue Gu, Ziheng Peng +6
Deep learning models for meteorological forecasting often fail in rare but high-impact events such as typhoons, where relevant data is scarce. Existing fine-tuning methods typicall…