Publications

Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play

Leyang Shen, Yang Zhang, Xiaoyan Zhao, Chun Kai Ling, Tat-Seng Chua

CARL: Criticality-Aware Agentic Reinforcement Learning

Leyang Shen, Yang Zhang, Chun Kai Ling, Xiaoyan Zhao, Tat-Seng Chua

A criticality-aware reinforcement learning algorithm for long-horizon agentic reasoning that focuses training on high-criticality states.

LION-FS: Fast & Slow Video-Language Thinker as Online Video Assistant

Wei Li, Bing Hu, Rui Shao, Leyang Shen, Liqiang Nie

In this work, we propose “Fast & Slow Video-Language Thinker” as onLIne videO assistaNt, LION-FS, achieving real-time, proactive, temporally accurate, and contextually precise responses.

MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models

Leyang Shen, Gongwei Chen, Rui Shao, Weili Guan, Liqiang Nie

In this work, we proposed a mixture of multimodal experts (MoME) framework to mitigate task interference and obtain a generalist MLLM.

LION: Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge

Gongwei Chen, Leyang Shen, Rui Shao, Xiang Deng, Liqiang Nie

In this work, we enhance MLLMs by integrating fine-grained spatial-aware visual knowledge and high-level semantic visual evidence, boosting capabilities and alleviating hallucinations.