5 papers
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
Can an Individual Manipulate the Collective Decisions of Multi-Agents?
Fengyuan Liu, Rui Zhao, Shuo Chen +4
Individual Large Language Models (LLMs) have demonstrated significant capabilities across various domains, such as healthcare and law. Recent studies also show that coordinated mul…
Tackling Data Corruption in Offline Reinforcement Learning via Sequence Modeling
Jiawei Xu, Rui Yang, Shuang Qiu +4
Learning policy from offline datasets through offline reinforcement learning (RL) holds promise for scaling data-driven decision-making while avoiding unsafe and costly online inte…
Self-playing Adversarial Language Game Enhances LLM Reasoning
Pengyu Cheng, Tianhao Hu, Han Xu +6
We explore the potential of self-play training for large language models (LLMs) in a two-player adversarial language game called Adversarial Taboo. In this game, an attacker and a…
EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents
Zihao Zhu, Bingzhe Wu, Zhengyou Zhang +3
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. The emergence of foundation models as the "brain" of EAI age…