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cs.AI2025
CuDIP: Enhancing Theorem Proving in LLMs via Curriculum Learning-based Direct Preference Optimization
Shuming Shi, Ruobing Zuo, Gaolei He +3
Automated theorem proving (ATP) is one of the most challenging mathematical reasoning tasks for Large Language Models (LLMs). Most existing LLM-based ATP methods rely on supervised…
cs.AI2024
A Context-Enhanced Framework for Sequential Graph Reasoning
Shuo Shi, Chao Peng, Chenyang Xu +1
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 grap…