22 citations · 54 across the 36 of their papers we have counts for
6 papers · 2 filters
Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models
Tongyan Hu, Hao Li, Xiaogeng Liu +8
Large language models remain vulnerable to jailbreaks, and automated red teaming is the standard way to find jailbreaks in large language models at scale. Current methods either dr…
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
Jundong Xu, Qingchuan Li, Jiaying Wu +11
Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deploymen…
SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization
Qi Zhang, Zhaopeng Feng, Xiaonan Shi +8
Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at inference time. However, cur…
Better with Experience: Self-Evolving LLM Agents for Evidence-Grounded Health Community Notes
Zihang Fu, Fanxiao Li, Jianyang Gu +5
Large Language Model (LLM)-augmented Community Notes offer a scalable path for timely, evidence-grounded correction of health misinformation on social platforms. However, they stil…
AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents
Zhaopeng Feng, Liangcai Su, Zhen Zhang +16
As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing cont…
Conversation for Non-verifiable Learning: Self-Evolving LLMs through Meta-Evaluation
Yuan Sui, Bryan Hooi
Training large language models (LLMs) for non-verifiable tasks, such as creative writing, dialogue, and ethical reasoning, remains challenging due to the absence of ground-truth la…