CombiBench: Benchmarking LLM Capability for Combinatorial Mathematics

1 code implementation • 6 May 2025 • Junqi Liu, Xiaohan Lin, Jonas Bayer, Yael Dillies, Weijie Jiang, Xiaodan Liang, Roman Soletskyi, Haiming Wang, Yunzhou Xie, Beibei Xiong, Zhengfeng Yang, Jujian Zhang, Lihong Zhi, Jia Li, Zhengying Liu

CombiBench is suitable for testing IMO solving capabilities since it includes all IMO combinatorial problems since 2000 (except IMO 2004 P3 as its statement contain an images).

Benchmarking

Automated Proof of Polynomial Inequalities via Reinforcement Learning

1 code implementation • 9 Mar 2025 • Banglong Liu, Niuniu Qi, Xia Zeng, Lydia Dehbi, Zhengfeng Yang

Current traditional algebraic methods are based on searching for a polynomial positive definite representation over a set of basis.

reinforcement-learning Reinforcement Learning +1

A Combinatorial Identities Benchmark for Theorem Proving via Automated Theorem Generation

no code implementations • 25 Feb 2025 • Beibei Xiong, Hangyu Lv, Haojia Shan, Jianlin Wang, Zhengfeng Yang, Lihong Zhi

Large language models (LLMs) have significantly advanced formal theorem proving, yet the scarcity of high-quality training data constrains their capabilities in complex mathematical domains.

Automated Theorem Proving Language Modeling +2

Open-Book Neural Algorithmic Reasoning

1 code implementation • 30 Dec 2024 • Hefei Li, Chao Peng, Chenyang Xu, Zhengfeng Yang

Neural algorithmic reasoning is an emerging area of machine learning that focuses on building neural networks capable of solving complex algorithmic tasks.

A Context-Enhanced Framework for Sequential Graph Reasoning

1 code implementation • 12 Dec 2024 • Shuo Shi, Chao Peng, Chenyang Xu, Zhengfeng Yang

The paper studies sequential reasoning over graph-structured data, which stands as a fundamental task in various trending fields like automated math problem solving and neural graph algorithm learning, attracting a lot of research interest.

Math

Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-Resolution

2 code implementations • CVPR 2023 • Jiahao Chao, Zhou Zhou, Hongfan Gao, Jiali Gong, Zhengfeng Yang, Zhenbing Zeng, Lydia Dehbi

In recent years, there has been an increasing demand for real-time super-resolution networks on mobile devices.

Image Super-Resolution

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

2 code implementations • 7 Nov 2022 • Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen, Gen Li, Yuanfan Zhang, Lei Sun, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu, Ao Li, Lei Luo, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He

While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.

Image Super-Resolution