Xiangchen Song (宋祥辰)
I am a PhD student in Machine Learning Department at Carnegie Mellon University, advised by Prof. Kun Zhang (CMU-CLeaR Group). Previously, I studied Computer Science at UIUC with Prof. Jiawei Han.
research
Large language models are sequence models, and my work aims to make their internal representations provably identifiable so we can interpret and steer model behavior with principled guarantees. I build on causal representation learning to recover latent structure in temporal data (time series, video, and text), and bring this lens to mechanistic interpretability of LLMs: designing identifiable sparse autoencoders with feature consistency for LLM activations, analyzing internal reasoning mechanisms, and enabling targeted control for more reliable and efficient model behavior.
contact
Email: xiangchs [at] cs [dot] cmu [dot] edu
news
| Sep 23, 2025 | Two papers about efficient LLM reasoning have been accepted to NeurIPS 2025 Workshop on Efficient Reasoning (ER@NeurIPS’2025)!! |
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| Sep 22, 2025 | Two papers on mechanistic interpretability have been accepted to Mechanistic Interpretability Workshop at NeurIPS 2025 (MechInterp@NeurIPS’2025)!! |
| Sep 18, 2025 | One paper “LLM Interpretability with Identifiable Temporal-Instantaneous Representation” has been accepted to The Thirty-ninth Conference on Neural Information Processing Systems (NeurIPS’2025)!! |
| May 01, 2025 | One paper “Reflection-Window Decoding: Text Generation with Selective Refinement” been accepted to The Forty-Second International Conference on Machine Learning (ICML’2025)!! |
| Jan 22, 2025 | One paper “On the Identification of Temporal Causal Representation with Instantaneous Dependence” has been accepted to The Thirteenth International Conference on Learning Representations (ICLR’2025) with oral presentation!! |