Welcome!

Hi! My name is Xin Liang (梁馨). I'm an incoming PhD student at Indiana University, advised by Prof. Jiangpeng He. Before that, I graduated from Tongji University with a Bachelor of Engineering in Computer Science, and I'm currently interning at Stepfun.

I’m deeply engaged in generative modeling and multimodal learning, with an emphasis on reliable, scalable methods for large multimodal models and LLM-based agents. More specifically, I focus on:

Representation learning and interpretability in VLMs

I am very interested in making adapters transparent so that we can see which features drive task-relevant behavior. This led to STAN, an input-adaptive sparse adapter that selects a small set of latent units via top-k regularization and exposes unit specialization through activation statistics and visualization.

Simulation of synthetic-data training cycles and bias evaluation

I am interested in how synthetic data reshapes future training, bias, and robustness across generations. We designed a multi-generation simulation with subgroup construction and iterative retraining. We find that with cleaner data, stronger models, and balanced generation, bias recedes; with noisier data, weaker models, or imbalanced sampling, it stacks up and stability suffers.

Modular LLM agents

I work on LLM agents, with interests in planning, tool use, memory, and multi-agent coordination. We built SlideGen, a multi-agent visual-in-the-loop pipline that converts research papers into well-structured slide decks.

You can browse my projects and CV. Feel free to reach out via email.