🧬 We just published our comprehensive analysis of OpenEvolve - an open-source evolutionary coding agent that automatically optimizes algorithms using LLMs!
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🧬 We just published our comprehensive analysis of OpenEvolve - an open-source evolutionary coding agent that automatically optimizes algorithms using LLMs!
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Our key findings from 29 experiments across 10 models:
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Our key findings from 29 experiments across 10 models:
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- Gemini Flash 2.5 achieved 2.04x speedup across 30 benchmark tasks
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- Gemini Flash 2.5 achieved 2.04x speedup across 30 benchmark tasks
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- Open models like Gemma 3 27B (1.63x) and Qwen3-Coder 480B (1.41x) rivaled proprietary models
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- Open models like Gemma 3 27B (1.63x) and Qwen3-Coder 480B (1.41x) rivaled proprietary models
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- The system discovered entirely new algorithms - not just code optimizations! 
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- The system discovered entirely new algorithms - not just code optimizations! 
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- One task evolved from DFS to BFS to Union-Find approaches
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- One task evolved from DFS to BFS to Union-Find approaches
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- Specialized coding models outperformed much larger general models 200 iterations beat 100 iterations by 24%
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- Specialized coding models outperformed much larger general models 200 iterations beat 100 iterations by 24%
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- Ensembles surprisingly failed due to conflicting optimization strategies
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- Ensembles surprisingly failed due to conflicting optimization strategies
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Most fascinating: watching models evolve code step-by-step, like transforming matrix operations from basic eigendecomposition to vectorized one-liners with 32x speedup.
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Most fascinating: watching models evolve code step-by-step, like transforming matrix operations from basic eigendecomposition to vectorized one-liners with 32x speedup.
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Our systematic experimental approach reveals that open-source evolutionary coding is becoming seriously competitive with proprietary solutions. We tested everything from temperature settings to evolution strategies to find optimal configurations.
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Our systematic experimental approach reveals that open-source evolutionary coding is becoming seriously competitive with proprietary solutions. We tested everything from temperature settings to evolution strategies to find optimal configurations.
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This research shows automated code optimization is ready for real-world applications. The methodology we developed can guide anyone building evolutionary coding systems.
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This research shows automated code optimization is ready for real-world applications. The methodology we developed can guide anyone building evolutionary coding systems.
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Full paper with code examples, detailed methodology, and all experimental results: 
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Full paper with code examples, detailed methodology, and all experimental results: 
https://huggingface.co/blog/driaforall/towards-open-evolutionary-agents
https://huggingface.co/blog/driaforall/towards-open-evolutionary-agents
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What optimization challenges could benefit from evolutionary approaches in your work?
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What optimization challenges could benefit from evolutionary approaches in your work?
🧬 We just published our comprehensive analysis of OpenEvolve - an open-source evolutionary coding agent that automatically optimizes algorithms using LLMs!

Our key findings from 29 experiments across 10 models:

- Gemini Flash 2.5 achieved 2.04x speedup across 30 benchmark tasks
- Open models like Gemma 3 27B (1.63x) and Qwen3-Coder 480B (1.41x) rivaled proprietary models
- The system discovered entirely new algorithms - not just code optimizations! 
- One task evolved from DFS to BFS to Union-Find approaches
- Specialized coding models outperformed much larger general models 200 iterations beat 100 iterations by 24%
- Ensembles surprisingly failed due to conflicting optimization strategies

Most fascinating: watching models evolve code step-by-step, like transforming matrix operations from basic eigendecomposition to vectorized one-liners with 32x speedup.

Our systematic experimental approach reveals that open-source evolutionary coding is becoming seriously competitive with proprietary solutions. We tested everything from temperature settings to evolution strategies to find optimal configurations.

This research shows automated code optimization is ready for real-world applications. The methodology we developed can guide anyone building evolutionary coding systems.

Full paper with code examples, detailed methodology, and all experimental results: https://huggingface.co/blog/driaforall/towards-open-evolutionary-agents

What optimization challenges could benefit from evolutionary approaches in your work?
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