初始化项目，由ModelHub XC社区提供模型

Model: Mercury7353/MetaAgent-X
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+  - en
+tags:
+  - large-language-model
+  - multi-agent-systems
+  - reinforcement-learning
+  - agentic-ai
+  - code
+  - math
+---
+
+# MetaAgent-X: Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning
+
+[Paper 📑](https://arxiv.org/abs/2605.14212)
+
+[Codebase 🚗](https://github.com/pettingllms-ai/PettingLLMs)
+
+[Project Page 🏆](https://mercury7353.github.io/MetaAgent-X-Page/)
+
+## Overview
+
+**MetaAgent-X** is an end-to-end reinforcement learning framework for autonomous multi-agent systems.
+
+Unlike conventional automatic MAS methods that rely on frozen models, hand-crafted prompts, or search-based workflows, MetaAgent-X trains one shared model to both **design** a multi-agent system and **execute** it. The model learns to generate task-adaptive agent roles, collaboration structures, and execution strategies through reinforcement learning.
+
+MetaAgent-X demonstrates strong cross-domain adaptation and achieves state-of-the-art performance across both **code** and **math** benchmarks.
+
+## Key Features
+
+- **One model for both design and execution**: the same model acts as both the MAS designer and the task executor.
+- **End-to-end reinforcement learning**: the model is optimized directly from downstream task outcomes.
+- **Autonomous multi-agent system generation**: the model learns to construct and execute agent swarms for complex reasoning tasks.
+- **Cross-domain generalization**: strong performance on both coding and mathematical reasoning benchmarks.
+
+## Results
+
+The following table reports the performance of **MetaAgent-X<sub>RL</sub>**.  
+Numbers in parentheses denote absolute gains over the single-agent baseline.
+
+| Domain | Benchmark | MetaAgent-X<sub>RL</sub> |
+|---|---:|---:|
+| Code | LiveCodeBench | **41.00**  |
+| Code | APPS | **38.00**  |
+| Code | CodeContests | **17.00**  |
+| Math | AIME24 | **40.00**  |
+| Math | AIME25 | **33.33**  |
+| Math | OlympiadBench | **61.00**  |
+| Overall | Average | **38.33** |
+
+## Citation
+```
+@misc{zhang2026metaagentxbreakingceiling,
+      title={MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning}, 
+      author={Yaolun Zhang and Yujie Zhao and Nan Wang and Yiran Wu and Jiayu Chang and Yizhao Chen and Qingyun Wu and Jishen Zhao and Huazheng Wang},
+      year={2026},
+      eprint={2605.14212},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2605.14212}, 
+}
+```