Algorithms. Physics. Mathematics. Machine Learning.
ICFPC2026. Postmortem. AI, LLM, Agents. Unagi team
Instead of posts "I ran one-shot prompt, it ran for 10 hours and gave me Counter Strike" I prefer to write about what agents didn't manage to solve. Why?
First of all, because it's boring. Nowadays all channels are about "Agents and LLM can do everything". It is not true. And it is important to understand, what they can't.
For example, it is not enough just to use LLM or agent to have good results in contests. One year ago I totally forgot about the contest and my participation was reduced to copy-pasting code and text from chatgpt to contest platform forth and back. In the tablet. About three hours during the taxi ride. And the result wasn't bad at all. 30th place.
This time two 100$ subscriptions, tough juggling with 5 agents, collaboration with friends gave 40th place.
Let's check what you should have to be at the top. I checked repository of Unagi team.
🏋️ Six participants with roles: autonomous agent system; iteration over solutions; devops; tools; hand-crafted solutions; holistic solutions; catalogue + room compiler
🏋️ Infrastructure prepared in advance: repository, CPU computational farm, AI agent collaboration tool
🏋️ Careful review and "lessons learned" from previous contests
I'm going to dive deeper into their machinery.
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