LLM4AAMAS: minor modificiation

README.md

@@ -52,14 +52,6 @@ to generative AAMAS. This list is a work in progress and will be regularly updat
     **[A Survey of Large Language Models](https://arxiv.org/abs/2303.18223)**
     Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. (2024) Published on *arXiv*
 
-- Based on the planning and reasoning abilities of LLM, the paper consider
-  LLM-based multi-agent systems for complex problem-solving and world
-  simulation.
-
-    **[Large Language Model based Multi-Agents: A Survey of Progress and
-    Challenges](https://arxiv.org/abs/2402.01680)** Taicheng Guo et al. (2024)
-    Published on *arXiv* arXiv:2402.01680 [cs.CL]
-
 - A framework for achieving strong natural language understanding with a single
   task-agnostic model through generative pre-training and discriminative
   fine-tuning.
@@ -300,6 +292,14 @@ these top-tier models and their OSS competitors.
 
 ## Generative MAS
 
+Based on the planning and reasoning abilities of LLM, the paper considers
+LLM-based multi-agent systems for complex problem-solving and world
+simulation.
+
+- **[Large Language Model based Multi-Agents: A Survey of Progress and
+  Challenges](https://arxiv.org/abs/2402.01680)** Taicheng Guo et al. (2024)
+  Published on *arXiv* arXiv:2402.01680 [cs.CL]
+
 LLMs can simulate realistic perceptions, reasoning, and decision-making, react
 adaptively to environments without predefined explicit instructions by adjusting
 their responses through contextual learning mechanisms, autonomously generate