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cs.LG2025
Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent
Bo Chen, Zhenmei Shi, Zhao Song +1
Recent advancements in Transformer-based architectures have led to impressive breakthroughs in natural language processing tasks, with models such as GPT-4, Claude, and Gemini demo…
cs.LG2025
Visual Autoregressive Transformers Must Use Memory
Yang Cao, Xiaoyu Li, Yekun Ke +3
A fundamental challenge in Visual Autoregressive models is the substantial memory overhead required during inference to store previously generated representations. Despite various…
cs.LG2025
Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling
Yang Cao, Bo Chen, Xiaoyu Li +5
This paper introduces Force Matching (ForM), a novel framework for generative modeling that represents an initial exploration into leveraging special relativistic mechanics to enha…