3 citations · 8 across the 5 of their papers we have counts for
6 papers
WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents
Siyu Zhou, Tianyi Zhou, Yijun Yang +4
Can we build accurate world models out of large language models (LLMs)? How can world models benefit LLM agents? The gap between the prior knowledge of LLMs and the specified envir…
Multi-Agent Coordination across Diverse Applications: A Survey
Lijun Sun, Yijun Yang, Qiqi Duan +5
Multi-agent coordination studies the underlying mechanism enabling the trending spread of diverse multi-agent systems (MAS) and has received increasing attention, driven by the exp…
WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents
Siyu Zhou, Tianyi Zhou, Yijun Yang +4
Can large language models (LLMs) directly serve as powerful world models for model-based agents? While the gaps between the prior knowledge of LLMs and the specified environment's…
Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld
Yijun Yang, Tianyi Zhou, Kanxue Li +6
While large language models (LLMs) excel in a simulated world of texts, they struggle to interact with the more realistic world without perceptions of other modalities such as visu…
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
Yijun Yang, Tianyi Zhou, Jing Jiang +2
How to train a generalizable meta-policy by continually learning a sequence of tasks? It is a natural human skill yet challenging to achieve by current reinforcement learning: the…
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
Qiqi Duan, Guochen Zhou, Chang Shao +7
In this paper, we present an open-source pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods (e.g., evolutionary algorithms, swarm intel…