Peng Cui

SARAD proposes a novel safety-aware hybrid framework that combines Large Language Models (LLMs) and Deep Reinforcement Learning (DRL) to improve autonomous driving decision-making by replacing random exploration with expert-guided decisions and adding collision prediction.

BayesNCL introduces a probabilistic gating mechanism to resolve the optimization conflict in Contrastive Learning, leading to highly disentangled and semantically consistent representations.

The paper introduces Archon, a unified, fully pretrained multimodal model that addresses the challenge of generating holistic digital humans by integrating seven modalities (including text, audio, motion, and visual content) into a single autoregressive framework.

The paper introduces Individual Fairness-aware Strategic Classification (IFSC), a framework that models interdependent strategic manipulation where agents imitate nearby positively decided peers to achieve favorable outcomes while maintaining individual fairness.