5 papers
A neural network for modeling human concept formation, understanding and communication
Liangxuan Guo, Haoyang Chen, Yang Chen +2
A remarkable capability of the human brain is to form more abstract conceptual representations from sensorimotor experiences and flexibly apply them independent of direct sensory i…
Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration
Liangxuan Guo, Bin Zhu, Qingqian Tao +5
Autonomous agents for desktop automation struggle with complex multi-step tasks due to poor coordination and inadequate quality control. We introduce Agentic Lybic, a novel multi-a…
Efficient Reinforcement Learning Through Adaptively Pretrained Visual Encoder
Yuhan Zhang, Guoqing Ma, Guangfu Hao +3
While Reinforcement Learning (RL) agents can successfully learn to handle complex tasks, effectively generalizing acquired skills to unfamiliar settings remains a challenge. One of…
Out-of-distribution forgetting: vulnerability of continual learning to intra-class distribution shift
Liangxuan Guo, Yang Chen, Shan Yu
Continual learning (CL) is an important technique to allow artificial neural networks to work in open environments. CL enables a system to learn new tasks without severe interferen…
Emergence of Symbols in Neural Networks for Semantic Understanding and Communication
Yang Chen, Liangxuan Guo, Shan Yu
The capacity to generate meaningful symbols and effectively employ them for advanced cognitive processes, such as communication, reasoning, and planning, constitutes a fundamental…