Author

Kun Feng

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#artificial intelligence Preprint Sep 2026

ARISE: Adapting to Evolving Capability Gaps in Agentic Reinforcement Learning

Adaptive Rubric-Skill Co-Evolution (ARISE), a reinforcement learning framework that uses rollout evidence to continually adapt evaluation criteria, exploration guidance, and training priorities, and demonstrates that ARISE successfully enhances both overall task performance and training efficiency.

Kun Feng, Yu-Chen Fang, Yih-Yang Tan et al. · 0 citations

#artificial intelligence May 2026

KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning

KairosAgent, a novel agentic framework for multimodal time series forecasting, including an LLM-based reasoner and a TSFM-based forecaster, unifies textual reasoning and numerical forecasting by dynamically invoking analytical tools to enhance the numerical understanding and semantic reasoning capabilities of LLMs.

Kun Feng, Ziwei Shan, Yu-Chen Fang et al. · arXiv.org · 5 citations · ⚡1