3 papers
cs.LG2026
The Topological Trouble With Transformers
Michael C. Mozer, Shoaib Ahmed Siddiqui, Rosanne Liu
Transformers encode structure in sequences via an expanding contextual history. However, their purely feedforward architecture fundamentally limits dynamic state tracking. State tr…
cs.CL2026
CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning
Congmin Zheng, Jiachen Zhu, Jianghao Lin +6
Process Reward Models (PRMs) play a central role in evaluating and guiding multi-step reasoning in large language models (LLMs), especially for mathematical problem solving. Howeve…
cs.CL2024
Improve Mathematical Reasoning in Language Models by Automated Process Supervision
Liangchen Luo, Yinxiao Liu, Rosanne Liu +9
Complex multi-step reasoning tasks, such as solving mathematical problems or generating code, remain a significant hurdle for even the most advanced large language models (LLMs). V…