2 citations · 5 across the 19 of their papers we have counts for
8 papers · 1 filter
From Bits to Beliefs: Recoverable Semantic Fingerprints for Black-Box Verification of Large Language Models
Jiaxin Hong, Yuxin Peng, Hongyao Yu +3
Open-weight large language models (LLMs) can be copied, modified, and redeployed behind black-box APIs, making post-release ownership verification difficult. Existing black-box fin…
Weak Ties, Strong Signals: Efficient Training Data Detection in Diffusion LLMs via Independent Token Sampling
Hongyao Yu, Tianqu Zhuang, Ziyuan Xu +4
Diffusion large language models (dLLMs) offer a compelling alternative to autoregressive models, yet they may expose sensitive training data during denoising. Detecting such usage…
MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents
Yv Zhang, Hao Sun, Hao Fang +5
External memory has become a core component of modern web agents, enabling long-horizon reasoning through the retrieval of past experiences. However, this paradigm introduces a cri…
Bypassing Copyright Protection in Diffusion-based Customization via Two-Stage Latent Feature Optimization
Ziang Xu, Wenbo Yu, Hongyao Yu +6
With the growing concerns over copyright infringement in diffusion-based customization, adversarial attacks have emerged as a prominent defense strategy to prevent malicious conten…
Reasoning Matters: Mitigate Hallucination in Multimodal Large Reasoning Models via Reasoning-Conditioned Preference Optimization
Jiawei Kong, Hao Fang, Shunxiang Liao +5
Multimodal Large Reasoning Models introduce the reasoning paradigm, demonstrating strong capabilities on complex vision-language tasks. However, they still suffer from severe hallu…
Seeing Through the Chain: Understanding and Mitigating Hallucinations in Multimodal Large Reasoning Models
Hao Fang, Jinyu Li, Jiawei Kong +4
While multimodal reasoning models (MLRMs) have exhibited impressive capabilities, they remain prone to hallucinations, and effective solutions are still underexplored. In this pape…
Looking Back and Forth: Cross-Image Attention Calibration and Attentive Preference Learning for Multi-Image Hallucination Mitigation
Xiaochen Yang, Hao Fang, Jiawei Kong +3
Although large vision-language models (LVLMs) have demonstrated remarkable capabilities, they are prone to hallucinations in multi-image tasks. We attribute this issue to limitatio…
Towards Distillation-Resistant Large Language Models: An Information-Theoretic Perspective
Hao Fang, Tianyi Zhang, Tianqu Zhuang +6
Proprietary large language models (LLMs) embody substantial economic value and are generally exposed only as black-box APIs, yet adversaries can still exploit their outputs to extr…