Yunxin Liu

Training Skills Like Parameters via Self-Supervised Semantic Diffusion

Experimental results demonstrate that this novel, unsupervised self-evolving agent framework inspired by the corruption-and-reconstruction paradigm of diffusion models enables the agent to autonomously extract and internalize highly generalizable skills, significantly enhancing its domain-specific generation capabilities.