Author

Ruizhi Qiao

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Preprint Aug 2026

From Atomic to Agentic: Towards Interpretable Evaluation of LLMs'Agentic Mathematical Capabilities

Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles, demonstrating that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.

Jiayi Kuang, Yinghui Li, Yun-Ze Song et al. · 0 citations

Preprint Jul 2026

RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents

Reinforcement learning holds significant potential for training large language models to handle multi-turn interactive tasks, but directly training with outcome rewards often results in slow convergence due to the sparsity of signals and the lack of fine-grained feedback.

Qiang Liu, Taian Guo, Ruizhi Qiao et al. · 0 citations