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The International Olympiad in Artificial Intelligence is the IMO of the AI era. At IOAI 2026 in Astana, 471 contestants from 108 countries and territories sat expert-designed problems spanning computer vision, language and optimization, all under a single-GPU budget. In 2026 the olympiad opened a second arena. IOAI², the AI Model Track, puts AI systems in the same exam hall: the same six contest tasks, two fully autonomous six-hour sessions of three tasks each, up to 50 submission attempts per task, every solution scored on standardized single-GPU hardware. Once a session starts, no human may solve, correct, or improve anything. Fourteen AI labs entered as Founding AI Participants. Kapso was one of them.

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Results

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Per task

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How Kapso runs it

Three stages, with the campaign clock starting at brief-in.
  1. Preflight. One agent session ingests the official task brief, downloads the data, and writes the task statement.
  2. Campaign. The experimentation loop — ideation, implementation, judged feedback — runs in parallel lanes. Each lane cycles submit-and-learn rounds through the official submission system: predict the score, submit, bank the result, study the gap, go again.
  3. Shared learning. Lanes learn from every sibling submission on the board, and ideas are grounded in a lesson bank distilled from past olympiad tasks.
The submit-and-learn loop is what the format rewards. With up to 50 attempts per task and a live score after each one, the useful unit of work is not a single good solution but a fast cycle between prediction and measurement.

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Usage

Ingest a task, from a URL or an organizer brief, into a run root:
Run the campaign for one six-hour session:

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Layout

Full integration notes are in benchmarks/ioai2026/.

MLE-Bench

Kaggle machine-learning competitions

RelBench

Predictive tasks over relational databases
Related pages: MLE-Bench · RelBench · MLE-Bench Kapso is an open-source framework by Leeroo that builds software toward measurable goals through experiment campaigns. Source code: github.com/Leeroo-AI/kapso · Install: pip install leeroo-kapso · Every page as plain text: llms.txt.