README logos

Author: alexzhang13

README.md

@@ -151,7 +151,7 @@ If you use this code or repository in your research, please cite:
 ## RLMs in the Wild
 There are many amazing demos and production-ready use cases of RLMs. We provide a list of notable examples that explicitly use RLMs as a central piece of their design.
 * <img src="https://www.google.com/s2/favicons?domain=dspy.ai&sz=64" alt="DSPy" height="15" style="vertical-align: middle;"/> [DSPy.RLM](https://github.com/stanfordnlp/dspy) <a href="https://github.com/stanfordnlp/dspy/stargazers"><img src="https://badgen.net/github/stars/stanfordnlp/dspy?icon=github&label=Stars" height="15" alt="GitHub stars"></a>
-* [Ax](https://github.com/ax-llm/ax) <a href="https://github.com/ax-llm/ax/stargazers"><img src="https://badgen.net/github/stars/ax-llm/ax?icon=github&label=Stars" height="15" alt="GitHub stars"></a>
+* <img src="https://github.com/ax-llm.png" alt="Ax" height="15" style="vertical-align: middle;"/> [Ax](https://github.com/ax-llm/ax) <a href="https://github.com/ax-llm/ax/stargazers"><img src="https://badgen.net/github/stars/ax-llm/ax?icon=github&label=Stars" height="15" alt="GitHub stars"></a>
 * <img src="https://github.com/context-labs.png" alt="context-labs" height="15" style="vertical-align: middle;"/> [context-labs/HALO: **RLM-based Automatic Agent Optimization Loop**](https://github.com/context-labs/halo) <a href="https://github.com/context-labs/halo/stargazers"><img src="https://badgen.net/github/stars/context-labs/halo?icon=github&label=Stars" height="15" alt="GitHub stars"></a>
 * [viplismism/rlm-cli: **CLI for Recursive Language Models**](https://github.com/viplismism/rlm-cli) <a href="https://github.com/viplismism/rlm-cli/stargazers"><img src="https://badgen.net/github/stars/viplismism/rlm-cli?icon=github&label=Stars" height="15" alt="GitHub stars"></a>
 * <img src="https://www.google.com/s2/favicons?domain=alphaxiv.org&sz=64" alt="alphaXiv" height="15" style="vertical-align: middle;"/> [alphaXiv Official Blog. **Reinforcing Recursive Language Models**](https://www.alphaxiv.org/blog/reinforcement-learning-for-rlms#training)