Author

Hongxin Ding

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#artificial intelligence Preprint Sep 2026

The Imitation Game: When LLMs Learn to Reason Like Programs via Code-Centric Reasoning Data Synthesis

Large Language Models (LLMs) excel at programming tasks but frequently fail at deterministic, fine-grained reasoning in natural language, relying heavily on semantic approximations rather than robust symbolic execution. To bridge this gap, we propose MIMIC, a framework that leverages executable code as a rigorous mediu...

Jin-Yang Zhang, Wei-Bin Liao, Ke-Qin Bao et al. · 0 citations

#artificial intelligence Preprint Aug 2026

AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing

This work proposes AgenticRag-R1, a RL framework that deeply integrates reasoning, retrieval, and memory via a memory stack and fine-grained action space, supported by hierarchical action-aware rewards and an information-aware trajectory rejection strategy to enable effective long-horizon learning.

Xin-Ke Jiang, Yue Fang, Zhi-Bang Yang et al. · 2 citations