docs: add header link badges

Author: wxj630

README.md

@@ -5,9 +5,10 @@
 </div>
 
 <p align="center">
-  <a href="README.md">English</a> | <a href="README_ZH.md">中文</a> |
-  <a href="https://dataarctech.github.io/Bayesian-Agent/">Docs</a> |
-  <a href="https://github.com/DataArcTech/Bayesian-Agent">GitHub</a>
+  🌐 <a href="README.md">English</a> | 🇨🇳 <a href="README_ZH.md">中文</a> |
+  📚 <a href="https://dataarctech.github.io/Bayesian-Agent/">Docs</a> |
+  🐙 <a href="https://github.com/DataArcTech/Bayesian-Agent">GitHub</a> |
+  📄 arXiv Coming Soon
 </p>
 
 Bayesian-Agent is a Bayesian self-evolving layer for turning agent failures into reusable, evidence-weighted Skills and SOPs across agent frameworks and execution harnesses.

README_ZH.md

@@ -5,9 +5,10 @@
 </div>
 
 <p align="center">
-  <a href="README.md">English</a> | <a href="README_ZH.md">中文</a> |
-  <a href="https://dataarctech.github.io/Bayesian-Agent/">Docs</a> |
-  <a href="https://github.com/DataArcTech/Bayesian-Agent">GitHub</a>
+  🌐 <a href="README.md">English</a> | 🇨🇳 <a href="README_ZH.md">中文</a> |
+  📚 <a href="https://dataarctech.github.io/Bayesian-Agent/">Docs</a> |
+  🐙 <a href="https://github.com/DataArcTech/Bayesian-Agent">GitHub</a> |
+  📄 arXiv Coming Soon
 </p>
 
 Bayesian-Agent 是一个面向跨 Agent framework / execution harness 的 Bayesian self-evolving layer，用于把 Agent 的失败轨迹转化为可复用、可验证、带 posterior 权重的 Skills 和 SOPs。

docs/index.md

@@ -4,6 +4,12 @@
   <img src="assets/banner.png" width="920" alt="Bayesian-Agent banner"/>
 </div>
 
+<p align="center">
+  📚 <a href="https://dataarctech.github.io/Bayesian-Agent/">Docs</a> |
+  🐙 <a href="https://github.com/DataArcTech/Bayesian-Agent">GitHub</a> |
+  📄 arXiv Coming Soon
+</p>
+
 Bayesian-Agent is a Bayesian self-evolving layer for turning verified agent trajectories into reusable, evidence-weighted Skills and SOPs across agent frameworks and execution harnesses.
 
 The project focuses on the inference side of agent improvement. Instead of changing base model parameters, it changes the agent's inference environment by maintaining posterior beliefs over Skills, failure modes, token cost, and context-specific reliability.