35 citations · 64 across the 38 of their papers we have counts for
39 papers
From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting
Yuanpu Cao, Yongkang Du, Yurui Chang +2
LLM agents are increasingly used for live forecasting, where they retrieve up-to-date information and produce estimates for unresolved future events. However, current agentic forec…
Can Factual Opinions Be Edited (Manipulated) in Large Language Models?
Yuanpu Cao, Ziyi Yin, Fenglong Ma +1
Large Language Models (LLMs) are increasingly integrated into various domains, making knowledge editing techniques crucial yet potentially hazardous. Current editing methods primar…
ForecastCompass: Guiding Agentic Forecasting with Adaptive Factor Memory
Yurui Chang, Yongkang Du, Yuanpu Cao +2
Agentic forecasting is important for decision-making in dynamic environments, but it remains challenging because agents must reason from incomplete, time-limited evidence and produ…
Restoring the Sweet Spot: Pass-Rate Weighted Self-Distillation for LLM Reasoning
Zehao Liu, Yuanpu Cao, Jinghui Chen +1
Self-Distillation Policy Optimization (SDPO) provides dense token-level credit assignment for reinforcement learning with large language models by leveraging the model's own feedba…
SkillGrad: Optimizing Agent Skills Like Gradient Descent
Hanyu Wang, Yifan Lan, Bochuan Cao +2
Agent skills provide a lightweight way to adapt LLM agents to specialized domains by storing reusable procedural knowledge in structured files. However, whether downloaded from thi…
The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation
Yifan Lan, Yuanpu Cao, Hanyu Wang +2
Large language models (LLMs) have demonstrated impressive reasoning abilities across a wide range of tasks, but data contamination undermines the objective evaluation of these capa…
PreFlect: From Retrospective to Prospective Reflection in Large Language Model Agents
Hanyu Wang, Yuanpu Cao, Lu Lin +1
Advanced large language model agents typically adopt self-reflection for improving performance, where agents iteratively analyze past actions to correct errors. However, existing r…
TASO: Jailbreak LLMs via Alternative Template and Suffix Optimization
Yanting Wang, Runpeng Geng, Jinghui Chen +2
Many recent studies showed that LLMs are vulnerable to jailbreak attacks, where an attacker can perturb the input of an LLM to induce it to generate an output for a harmful questio…
Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learning
Yujia Wang, Fenglong Ma, Jinghui Chen
Asynchronous federated learning (FL) has recently gained attention for its enhanced efficiency and scalability, enabling local clients to send model updates to the server at their…
ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models
Yujia Wang, Yuanpu Cao, Jinghui Chen
Federated learning (FL) has been extensively studied as a privacy-preserving training paradigm. Recently, federated block coordinate descent scheme has become a popular option in t…
You Can't Steal Nothing: Mitigating Prompt Leakages in LLMs via System Vectors
Bochuan Cao, Changjiang Li, Yuanpu Cao +3
Large language models (LLMs) have been widely adopted across various applications, leveraging customized system prompts for diverse tasks. Facing potential system prompt leakage ri…
Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time
Yifan Lan, Yuanpu Cao, Weitong Zhang +2
Recently, Multimodal Large Language Models (MLLMs) have gained significant attention across various domains. However, their widespread adoption has also raised serious safety conce…
Your Agent Can Defend Itself against Backdoor Attacks
Li Changjiang, Liang Jiacheng, Cao Bochuan +2
Despite their growing adoption across domains, large language model (LLM)-powered agents face significant security risks from backdoor attacks during training and fine-tuning. Thes…
AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
Xin Yu, Yujia Wang, Jinghui Chen +1
Low-Rank Adaptation (LoRA) has emerged as an effective technique for reducing memory overhead in fine-tuning large language models. However, it often suffers from sub-optimal perfo…
TruthFlow: Truthful LLM Generation via Representation Flow Correction
Hanyu Wang, Bochuan Cao, Yuanpu Cao +1
Large language models (LLMs) are known to struggle with consistently generating truthful responses. While various representation intervention techniques have been proposed, these m…
Towards Robust Multimodal Large Language Models Against Jailbreak Attacks
Ziyi Yin, Yuanpu Cao, Han Liu +3
While multimodal large language models (MLLMs) have achieved remarkable success in recent advancements, their susceptibility to jailbreak attacks has come to light. In such attacks…
Data Free Backdoor Attacks
Bochuan Cao, Jinyuan Jia, Chuxuan Hu +5
Backdoor attacks aim to inject a backdoor into a classifier such that it predicts any input with an attacker-chosen backdoor trigger as an attacker-chosen target class. Existing ba…
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models
Yaopei Zeng, Yuanpu Cao, Bochuan Cao +3
Recent advances in diffusion models have significantly enhanced the quality of image synthesis, yet they have also introduced serious safety concerns, particularly the generation o…
FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection
Jiaqi Wang, Xiaochen Wang, Lingjuan Lyu +2
This study introduces the Federated Medical Knowledge Injection (FEDMEKI) platform, a new benchmark designed to address the unique challenges of integrating medical knowledge into…
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model
Yuanpu Cao, Lu Lin, Jinghui Chen
Deep learning-based industrial anomaly detection models have achieved remarkably high accuracy on commonly used benchmark datasets. However, the robustness of those models may not…
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models
Xiaochen Wang, Jiaqi Wang, Houping Xiao +2
Foundation models have demonstrated remarkable capabilities in handling diverse modalities and tasks, outperforming conventional artificial intelligence (AI) approaches that are hi…
Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization
Yuanpu Cao, Tianrong Zhang, Bochuan Cao +4
Researchers have been studying approaches to steer the behavior of Large Language Models (LLMs) and build personalized LLMs tailored for various applications. While fine-tuning see…
FADAS: Towards Federated Adaptive Asynchronous Optimization
Yujia Wang, Shiqiang Wang, Songtao Lu +1
Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. While the SGD-based FL algorithms have demonstrated considerable…
Watch the Watcher! Backdoor Attacks on Security-Enhancing Diffusion Models
Changjiang Li, Ren Pang, Bochuan Cao +4
Thanks to their remarkable denoising capabilities, diffusion models are increasingly being employed as defensive tools to reinforce the security of other models, notably in purifyi…