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AutoHarness: improving LLM agents by automatically synthesizing a code harness
arXiv – CS AI|Xinghua Lou, Miguel L\'azaro-Gredilla, Antoine Dedieu, Carter Wendelken, Wolfgang Lehrach, Kevin P. Murphy|
🤖AI Summary
Researchers developed AutoHarness, a technique where smaller LLMs like Gemini-2.5-Flash can automatically generate code harnesses to prevent illegal moves in games, outperforming larger models like Gemini-2.5-Pro and GPT-5.2-High. The method eliminates 78% of failures attributed to illegal moves in chess competitions and demonstrates superior performance across 145 different games.
Key Takeaways
- →Smaller LLMs can automatically synthesize code harnesses to prevent illegal actions that larger models frequently make.
- →The technique eliminated all illegal moves across 145 different TextArena games using iterative code refinement.
- →Gemini-2.5-Flash with AutoHarness outperformed larger models like Gemini-2.5-Pro and GPT-5.2-High on game tasks.
- →The method can generate entire policies in code, removing the need for LLM inference during decision-making.
- →This approach offers better performance while being more cost-effective than using larger models.
Mentioned in AI
Models
GeminiGoogle
#autoharness#llm-agents#code-synthesis#gemini#ai-efficiency#game-ai#cost-optimization#model-performance
Read Original →via arXiv – CS AI
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