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@@ -563,6 +563,21 @@ still show limitations in adaptability.
   Guan, Tao Ge, Furu Wei (2025) at *COLING 2025*.
 
 
+
+This analysis shows that LLMs often stray from rational strategies, especially
+as game complexity increases. Notably, the nature of their irrationality differs
+from that of humans. The study highlights a key limitation: LLMs struggle with
+complex strategic reasoning unless provided with explicit structural support. To
+address this, the authors propose game-theory-inspired workflows that guide
+LLMs’ reasoning using principles such as dominant strategies, backward
+induction, and Bayesian belief updating. Interestingly, the model that initially
+performed the worst without the workflow becomes the best with it — and vice
+versa.
+
+- **[Game-theoretic LLM: Agent Workflow for Negotiation Games](https://arxiv.org/abs/2411.05990)**  
+  Wenyue Hua, Ollie Liu, Lingyao Li, Alfonso Amayuelas, Julie Chen, Lucas Jiang, Mingyu Jin, Lizhou Fan, 
+  Fei Sun, William Wang, Xintong Wang, Yongfeng Zhang (2024) on *arXiv*.  
+
 ### Generative MAS on the shelf
 
 - [MetaGPT](https://github.com/geekan/MetaGPT) is a framework for creating