Author

Tian-Yi Tang

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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

Jul 2026

Are the Financial Reasoning from LLMs Credible? A Real World Test over Long-Horizon Statements

FinIndices is a large-scale benchmark evaluating data-processing fidelity over uncropped financial statements (up to 32K tokens) and yields substantial zero-hint gains, validating that structured logic can be partially restored via data-centric alignment.

Xin Tong, Xuanming Zhang, Tian-Yi Tang et al. · arXiv.org · 0 citations

Jul 2026

Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning

These findings suggest that, even as long-context evaluation shifts from simple retrieval toward complex reasoning, accurate grounding in relevant evidence remains an indispensable capability with substantial room for improvement.

Li-Zhe Fang, Weizhou Shen, Tian-Yi Tang et al. · arXiv.org · 0 citations