Author

Shengyuan Wang

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#artificial intelligence Preprint Oct 2026

Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition

Adversarial games have driven advances from heuristic search to reinforcement learning, yet learning and adapting strategies from limited samples remain challenging. AI agents offer an alternative by turning game experience into revisions of executable policies. Building on heuristic learning (HL), we formalize Adversa...

Kai-Sen Yang, Qing-Le Liu, Ke-Jin Wang et al. · 0 citations

Preprint Aug 2026

SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents

This paper introduces SkillTrace, which organizes the user query into a semantic hierarchy, matches skill queries and candidates, and propagates over the skill dependencies, and delivers consistent improvements across different backbone language models, demonstrating the generality and robustness of graph-based skill r...

Yue Yao, Shengyuan Wang, Xin Chen et al. · 0 citations

Review Open access Aug 2026

Reinforcement Learning in the Era of Large Language Models: Challenges and Opportunities

A systematic literature review on how RL are adapted and scaled as a fundamental post-training tools and how innovations in the RL pipeline enhance the domain-specific LLMs is conducted.

Qianyue Hao, Lin Chen, Xiao-Qian Qi et al. · ACM Computing Surveys · 1 citation