Jiang-Tian Xue — accepted AI-conference papers

2026

Dynamic-Static Synergistic Selection Method for Candidate Code Solutions with Generated Test Cases

Ren-Biao Liu, Jiang-Tian Xue, Chao-Zeng Ma, Hui Sun, Xin-Ye Li, Ming Li

AAAI 2026technical

Large language models (LLMs) show significant improvement in code generation. A common practice is sampling multiple candidate codes to increase the likelihood of producing an accurate solution. However, effectively identifying the best candidate from the pool is a significant challenge. Although ex

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2026

Random Selection Reveals Implicit Knowledge Consensus in Code Generation

Ren-Biao Liu, Li Xin-Ye, Hui Sun, Yali Du, Jiang-Tian Xue, Ming Li

ICML 2026poster

Training large language models for code generation requires selecting high-quality data from solution pools where each problem admits multiple correct implementations. Conventional studies on data selection hold that sophisticated strategies that employ various optimization objectives, such as diver…

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