  Disentangled representation learning (DRL) aims to identify and decompose
underlying factors behind observations, thus facilitating data perception and
generation. However, current DRL approaches often rely on the unrealistic
assumption that semantic factors are statistically independent. In reality,
these factors may exhibit correlations, which off-the-shelf solutions have yet
to properly address. To tackle this challenge, we introduce a bidirectional
weighted graph-based framework, to learn factorized attributes and their
interrelations within complex data. Specifically, we propose a $\beta$-VAE
based module to extract factors as the initial nodes of the graph, and leverage
the multimodal large language model (MLLM) to discover and rank latent
correlations, thereby updating the weighted edges. By integrating these
complementary modules, our model successfully achieves fine-grained, practical
and unsupervised disentanglement. Experiments demonstrate our method's superior
performance in disentanglement and reconstruction. Furthermore, the model
inherits enhanced interpretability and generalizability from MLLMs.
