6 papers
Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions
Qianyun Guo, Yibo Li, Yue Liu +1
Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactions. However, assessing how well L…
KLong: Training LLM Agent for Extremely Long-horizon Tasks
Yue Liu
This paper introduces KLong, an open-source LLM agent trained to solve extremely long-horizon tasks. The principle is to first cold-start the model via trajectory-splitting SFT, th…
EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic Systems
Yufei He, Juncheng Liu, Yue Liu +5
A fundamental limitation of current AI agents is their inability to learn complex skills on the fly at test time, often behaving like "clever but clueless interns" in novel environ…
VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents
Tri Cao, Bennett Lim, Yue Liu +7
Computer-Use Agents (CUAs) with full system access enable powerful task automation but pose significant security and privacy risks due to their ability to manipulate files, access…
UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs
Yufei He, Yuan Sui, Xiaoxin He +3
Existing foundation models, such as CLIP, aim to learn a unified embedding space for multimodal data, enabling a wide range of downstream web-based applications like search, recomm…
FlipAttack: Jailbreak LLMs via Flipping
Yue Liu, Xiaoxin He, Miao Xiong +5
This paper proposes a simple yet effective jailbreak attack named FlipAttack against black-box LLMs. First, from the autoregressive nature, we reveal that LLMs tend to understand t…