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20202026
most citedThe Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies

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

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6 papers · 1 filter

cs.AI2026

AgentSafeEvo: Adversarial Co-Evolution for LLM Agent Safety

Chenxu Wang, Xiaoyu Wen, Han Qi +4

Large language model (LLM) agents accomplish complex tasks through multi-turn interactions with external tools. However, malicious content embedded in user queries, tool descriptio…

cs.AI2026

Distilling Agentic Systems: A Roadmap across Models, Artifacts, and Harnesses

Ziluowen Luo, Senzhang Wang, Chaozhuo Li +9

Modern agents increasingly rely on memories, tools, and execution logic, so their competence extends beyond model parameters. This shift exposes a limitation of conventional knowle…

cs.AI2026

JailbreakSkill: Scaling Automated Red-Teaming with Reusable and Ever-Evolving Skills

Xiaoyu Wen, Jiajia Li, Zhida He +11

Automated red-teaming has produced a growing collection of attack strategies, yet they typically remain scattered across prompts and workflows, making them difficult to systematica…

cs.AI2026

LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing

Hao Li, Yiqun Zhang, Zhaoyan Guo +9

Large language model (LLM) routing assigns each query to the most suitable model from an ensemble. We introduce LLMRouterBench, a large-scale benchmark and unified framework for LL…

cs.AI2025

Learning Compact Representations of LLM Abilities via Item Response Theory

Jianhao Chen, Chenxu Wang, Gengrui Zhang +5

Recent years have witnessed a surge in the number of large language models (LLMs), yet efficiently managing and utilizing these vast resources remains a significant challenge. In t…

cs.AI2025

PerPilot: Personalizing VLM-based Mobile Agents via Memory and Exploration

Xin Wang, Zhiyao Cui, Hao Li +10

Vision language model (VLM)-based mobile agents show great potential for assisting users in performing instruction-driven tasks. However, these agents typically struggle with perso…