We are the team at Sakana AI. To give some context on the difficulty here, an OpenAI agent placed 2nd in the AHC world tournament last August, so taking 1st place against 804 humans in this contest is a significant milestone for us. Our agent approached the production planning problem by running its own experiments during the contest. It independently discovered a Simulated Annealing strategy using a "virtual power" heuristic which ended up outperforming the greedy solutions that the problem setters anticipated.
We used inference-time scaling with GPT-5.2 and Gemini 3 Pro Preview to make this happen. The agent ran parallel code generation loops to iteratively refine the algorithm, costing about $1,300 in total compute for the 4 hour event. We published the full logs showing the agent's analysis and code evolution at the link in the post.
Happy to answer any questions about the architecture!
hardmaru•1mo ago
We are the team at Sakana AI. To give some context on the difficulty here, an OpenAI agent placed 2nd in the AHC world tournament last August, so taking 1st place against 804 humans in this contest is a significant milestone for us. Our agent approached the production planning problem by running its own experiments during the contest. It independently discovered a Simulated Annealing strategy using a "virtual power" heuristic which ended up outperforming the greedy solutions that the problem setters anticipated.
We used inference-time scaling with GPT-5.2 and Gemini 3 Pro Preview to make this happen. The agent ran parallel code generation loops to iteratively refine the algorithm, costing about $1,300 in total compute for the 4 hour event. We published the full logs showing the agent's analysis and code evolution at the link in the post.
Happy to answer any questions about the architecture!
Blog Post with details: https://sakana.ai/ahc058
For more technical detailed information, including the logs and analysis output by ALE-Agent during the contest, see: https://sakanaai.github.io/fishylene-ahc058/