Papers by Minghao Ma
HiSVD: Principled Low-Rank Approximation of LLMs via Hierarchical Modeling of Information Capacity and Spectral Structure (2026.acl-long)
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| Challenge: | Existing methods for generating layer importance ignore the fine-grained influence of spectral distribution shape. |
| Approach: | They propose a hierarchical rank allocation framework with two stages to address this gap . they propose SVD-based lowrank approximation that exploits spectral heterogeneity . |
| Outcome: | Experiments show that HiSVD outperforms state-of-the-art methods on LLMs . |
CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization (2026.acl-long)
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Zhongyuan Peng, Yifan Yao, Kaijing Ma, Shuyue Guo, Yizhe Li, Yichi Zhang, Chenchen Zhang, Yifan Zhang, Zhouliang Yu, Luming Li, Minghao Liu, Yihang Xia, Jiawei Shen, Yuchen Wu, Yixin Cao, Zhaoxiang Zhang, Wenhao Huang, Jiaheng Liu, Ge Zhang
| Challenge: | Existing approaches to formalizing mathematical statements face limitations in accuracy, especially in the context of complex, highlevel problems that involve sophisticated mathematical reasoning. |
| Approach: | They propose a CriticLean framework that elevates the role of the critic from a passive validator to an active learning component and introduce a benchmark to measure models’ ability to distinguish semantically correct from incorrect formalizations. |
| Outcome: | The proposed framework outperforms open- and closed-source benchmarks and shows that it significantly outperformed existing models. |
ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming (2025.emnlp-main)
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| Challenge: | Constraint programming (CP) is a powerful paradigm for solving constraint optimization problems. |
| Approach: | They propose to use an open-source LLM to generate formal modeling for COPs. |
| Outcome: | The proposed model outperforms the baselines on the new IndusCP benchmark by 2x. |
MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction (2024.emnlp-main)
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| Challenge: | Existing approaches within the pretraining-finetuning paradigm tend to meticulously craft complex tagging schemes and classification heads, or incorporate external semantic enhancements to enhance performance. |
| Approach: | They propose to integrate a minimalist tagging scheme and a novel token-level contrastive learning strategy to improve pretrained representations. |
| Outcome: | The proposed framework achieves comparable or superior performance compared to state-of-the-art techniques while featuring a more compact design and reduced computational overhead. |