6 citations · 6 across the 7 of their papers we have counts for
7 papers
HoloBrain-0 Technical Report
Xuewu Lin, Tianwei Lin, Yun Du +12
In this work, we introduce HoloBrain-0, a comprehensive Vision-Language-Action (VLA) framework that bridges the gap between foundation model research and reliable real-world robot…
Benchmarking at the Edge of Comprehension
Samuele Marro, Jialin Yu, Emanuele La Malfa +8
As frontier Large Language Models (LLMs) increasingly saturate new benchmarks shortly after they are published, benchmarking itself is at a juncture: if frontier models keep improv…
Collaborative Belief Reasoning with LLMs for Efficient Multi-Agent Collaboration
Zhimin Wang, Duo Wu, Shaokang He +6
Effective real-world multi-agent collaboration requires not only accurate planning but also the ability to reason about collaborators' intents--a crucial capability for avoiding mi…
Detecting Instruction Fine-tuning Attacks using Influence Function
Jiawei Li
Instruction fine-tuning attacks pose a serious threat to large language models (LLMs) by subtly embedding poisoned examples in fine-tuning datasets, leading to harmful or unintende…
Predicting User Behavior in Smart Spaces with LLM-Enhanced Logs and Personalized Prompts
Yunpeng Song, Jiawei Li, Yiheng Bian +1
Enhancing the intelligence of smart systems, such as smart home, and smart vehicle, and smart grids, critically depends on developing sophisticated planning capabilities that can a…
Delta-Influence: Unlearning Poisons via Influence Functions
Wenjie Li, Jiawei Li, Pengcheng Zeng +3
Addressing data integrity challenges, such as unlearning the effects of data poisoning after model training, is necessary for the reliable deployment of machine learning models. St…
Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion
Jiawei Li, Sitong Li, Shanshan Wang +3
Deploying machine learning in open environments presents the challenge of encountering diverse test inputs that differ significantly from the training data. These out-of-distributi…