diff --git a/README.md b/README.md
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+++ b/README.md
@@ -1181,6 +1181,8 @@
- [Shiling42/web-simulator-by-GPT4](https://github.com/Shiling42/web-simulator-by-GPT4)
: Online Interactive Physical Simulation Generated by GPT-4. [shilingliang.com/web-simulator-by-GPT4/](https://shilingliang.com/web-simulator-by-GPT4/)
+ - [aiXiv](https://github.com/aixiv-org/aiXiv)
: "aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists". (**[arXiv 2025](https://arxiv.org/abs/2508.15126)**). An open-access platform with multi-agent architecture for AI and human scientists to submit, review, and refine research papers. [aiXiv Beta Waitlist](https://docs.google.com/forms/d/e/1FAIpQLSdd6nDoI5qQ1lf_mx352NmrSIeFhnIv7zLIjlXEJtiRMoo25A/viewform)
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@@ -1203,6 +1205,10 @@
- [仲景](https://github.com/SupritYoung/Zhongjing)
: 仲景:首个实现从预训练到 RLHF 全流程训练的中文医疗大模型。 "Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue". (**[arXiv 2023](https://arxiv.org/abs/2308.03549)**).
+ - [GenoTEX](https://github.com/Liu-Hy/GenoTEX)
: "GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis". (**[MLCB 2025](https://arxiv.org/abs/2406.15341)**). A benchmark for evaluating LLM agents on gene expression analysis tasks, including dataset selection, preprocessing, and statistical analysis. [liu-hy.github.io/GenoTEX](https://liu-hy.github.io/GenoTEX/)
+
+ - [GenoMAS](https://github.com/Liu-Hy/GenoMAS)
: "GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis". (**[arXiv 2025](https://arxiv.org/abs/2507.21035)**). A multi-agent framework integrating guided planning with autonomous error handling for genomic data analysis. [liu-hy.github.io/GenoMAS](https://liu-hy.github.io/GenoMAS/)
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@@ -1323,6 +1329,12 @@
- [OpenManus](https://github.com/mannaandpoem/OpenManus)
: No fortress, purely open ground. OpenManus is Coming.
+ - [xhyumiracle/Awesome-AgenticLLM-RL-Papers](https://github.com/xhyumiracle/Awesome-AgenticLLM-RL-Papers)
: "The Landscape of Agentic Reinforcement Learning for LLMs: A Survey". (**[arXiv 2025](https://arxiv.org/abs/2509.02547)**). A comprehensive survey on agentic RL for LLMs, covering core capabilities and applications.
+
+ - [CoMAS](https://github.com/Yiting-ZH/CoMAS) : "CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards". (**[arXiv 2025](https://arxiv.org/abs/2510.08529)**). A framework enabling autonomous agent improvement through inter-agent interactions and reinforcement learning.
+
+ - [Achilles Heel of DMAS](https://arxiv.org/abs/2504.07461) : "Achilles Heel of Distributed Multi-Agent Systems". (**[arXiv 2025](https://arxiv.org/abs/2504.07461)**). Studies trustworthiness challenges in distributed multi-agent systems with heterogeneous LLMs.
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