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AIMO Interpretability Challenge

arXiv cs.AI LLMs & Foundation Models Michal Štefánik, Philipp Mondorf, Andreas Waldis, Qianying Liu, Chuan Yang, Michal Spiegel, Josef Kuchař, Marek Kadlčík, Adam Vawda-Oomerjee, Chaoran Liu, Simon Frieder, Barbara Plank, Fazl Barez, Pontus Stenetorp 2026-07-15

TL;DR — A proposed competition (the AIMO Interpretability Challenge) that pushes beyond final-answer accuracy to judge whether frontier math-reasoning LLMs use robust mechanisms or brittle shortcuts, using interpretability and adversarial robustness. It matters because reliable reasoning, not just correct outputs, is central to trusting frontier models.

Motivated by a key benchmark limitation: high accuracy doesn't reveal if a model reasons stably or exploits spurious shortcuts.
Provides resources: new olympiad-level (AIMO) problems with symbolic representations for generating functional variants, access to frontier reasoning models, and adversarial robustness assessments, plus compute support.
Deliverables include an open robustness benchmark and baseline systems intended as a lasting standard for math-reasoning interpretability.
Frames a research question linking interpretability and generalization: can we determine whether frontier models' decision-making is generalizable and reliable? (Note: this is a challenge proposal, so no empirical results are reported.)

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