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+++ b/README.md
@@ -2,6 +2,8 @@
 
 An open-source implementation of the AlphaEvolve system described in the Google DeepMind paper "AlphaEvolve: A coding agent for scientific and algorithmic discovery" (2025).
 
+![OpenEvolve Logo](openevolve-logo.png)
+
 ## Overview
 
 OpenEvolve is an evolutionary coding agent that uses Large Language Models to optimize code through an iterative process. It orchestrates a pipeline of LLM-based code generation, evaluation, and selection to continuously improve programs for a variety of tasks.
@@ -18,11 +20,15 @@ Key features:
 
 OpenEvolve follows an evolutionary approach with the following components:
 
+![OpenEvolve Architecture](openevolve-architecture.png)
+
 1. **Prompt Sampler**: Creates context-rich prompts containing past programs, their scores, and problem descriptions
 2. **LLM Ensemble**: Generates code modifications via an ensemble of language models
 3. **Evaluator Pool**: Tests generated programs and assigns scores
 4. **Program Database**: Stores programs and their evaluation metrics, guiding future evolution
 
+The controller orchestrates interactions between these components in an asynchronous pipeline, maximizing throughput to evaluate as many candidate solutions as possible.
+
 ## Getting Started
 
 ### Installation
@@ -151,6 +157,18 @@ See the [Configuration Guide](configs/default_config.yaml) for a full list of op
 
 See the `examples/` directory for complete examples of using OpenEvolve on various problems:
 
+### Circle Packing
+
+Our implementation of the circle packing problem from the AlphaEvolve paper, where we successfully match their reported results within 0.04%.
+
+[Explore the Circle Packing Example](examples/circle_packing/)
+
+### Function Minimization
+
+An example showing how OpenEvolve can transform a simple random search algorithm into a sophisticated simulated annealing approach.
+
+[Explore the Function Minimization Example](examples/function_minimization/)
+
 ## Preparing Your Own Problems
 
 To use OpenEvolve for your own problems:
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