Xinzhe Ni — accepted AI-conference papers

2025

Unlocking Multimodal Mathematical Reasoning via Process Reward Model

Ruilin Luo, Zhuofan Zheng, Lei Wang, Yifan Wang, Xinzhe Ni, Zicheng Lin +6

NeurIPS 2025poster

Process Reward Models (PRMs) have shown promise in enhancing the mathematical reasoning capabilities of Large Language Models (LLMs) through Test-Time Scaling (TTS). However, their integration into multimodal reasoning remains largely unexplored. In this work, we take the first step toward unlocking…

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2024

Hint-Enhanced In-Context Learning Wakes Large Language Models Up For Knowledge-Intensive Tasks

Yifan Wang, Qingyan Guo, Xinzhe Ni, Chufan Shi, Lemao Liu, Haiyun Jiang +1

ICASSP 2024accepted

In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs), enabling them to learn input-label mappings from demonstrations and perform well on downstream tasks. However, under the standard ICL setting, LLMs may sometimes neglect query-related information…

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