Author

Jinpeng Li

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#artificial intelligence Preprint Sep 2026

Noisy Test-Time Reinforcement Learning for Code LLMs

Large language models (LLMs) have demonstrated remarkable performance across various code-related tasks. However, unlike carefully curated datasets that are typically high-quality and error-free, real-world user instructions are often vague and error-prone, posing significant challenges to the robustness of code LLMs....

Xi-Kai Yang, Hieu Trung Nguyen, Dun-Yuan Xu et al. · 0 citations

#machine learning Open access Sep 2025

Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view

OmniMol is presented, a framework using hypergraphs to improve predictions of molecular properties, addressing challenges of imperfect data annotation and enhancing model explainability, and achieves state-of-the-art performance in properties prediction.

Bowen Wang, Junyou Li, Donghao Zhou et al. · Nature Communications · 11 citations