4 citations · 4 across the 11 of their papers we have counts for
12 papers
Query-Adaptive Neuron Steering for LLMs
Yanxi Pan, Jingwen Wu, Yan Ma +8
Effectively eliciting the capabilities of large language models (LLMs) at inference time remains an important challenge. Existing studies address this challenge through context eng…
SIVA-RL: Sensitivity-Invariance Visual Alignment for Multimodal Reinforcement Learning
Cheng Tang, Junzhi Ning, Min Cen +9
Reinforcement learning with verifiable rewards (RLVR) drives multimodal reasoning, but answer-level correctness does not guarantee that a vision-language model grounds its predicti…
SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models
Xinyi Zeng, Xue Yang, Jingyuan Zhang +5
Multimodal large language models (MLLMs) are gaining increasing attention. Due to the heterogeneity of their input features, they face significant challenges in terms of jailbreak…
DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models
Lifan Zheng, Xue Yang, Jiawei Chen +6
With the widespread adoption of large language models (LLMs), understanding their personality representation mechanisms has become critical. As a novel paradigm in Personality Edit…
Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO
Yu Tian, Jiawei Chen, Lifan Zheng +5
We introduce Skills-Coach, a novel automated framework designed to significantly enhance the self-evolution of skills within Large Language Model (LLM)-based agents. Addressing the…
AdaCultureSafe: Adaptive Cultural Safety Grounded by Cultural Knowledge in Large Language Models
Hankun Kang, Di Lin, Zhirong Liao +6
With the global proliferation of Large Language Models (LLMs), cultural safety, defined as the ability to generate respectful and appropriate responses across diverse cultures, bec…
Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction
Shuoxin Wang, Chang Liu, Gowen Loo +5
Large Language Model (LLM)-based mobile agents have made significant performance advancements. However, these agents often follow explicit user instructions while overlooking perso…
DiffER: Diffusion Entity-Relation Modeling for Reversal Curse in Diffusion Large Language Models
Shaokai He, Kaiwen Wei, Xinyi Zeng +5
The "reversal curse" refers to the phenomenon where large language models (LLMs) exhibit predominantly unidirectional behavior when processing logically bidirectional relationships…
Rethinking the Reliability of Multi-agent System: A Perspective from Byzantine Fault Tolerance
Lifan Zheng, Jiawei Chen, Qinghong Yin +3
Ensuring the reliability of agent architectures and effectively identifying problematic agents when failures occur are crucial challenges in multi-agent systems (MAS). Advances in…
Feature-Aware Malicious Output Detection and Mitigation
Weilong Dong, Peiguang Li, Yu Tian +3
The rapid advancement of large language models (LLMs) has brought significant benefits to various domains while introducing substantial risks. Despite being fine-tuned through rein…
Root Defence Strategies: Ensuring Safety of LLM at the Decoding Level
Xinyi Zeng, Yuying Shang, Jiawei Chen +2
Large language models (LLMs) have demonstrated immense utility across various industries. However, as LLMs advance, the risk of harmful outputs increases due to incorrect or malici…
From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models
Yuying Shang, Xinyi Zeng, Yutao Zhu +6
Hallucinations in large vision-language models (LVLMs) are a significant challenge, i.e., generating objects that are not presented in the visual input, which impairs their reliabi…