Author

Chaoran Liu

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Preprint Jul 2026

AIMO Interpretability Challenge

We propose the AIMO Interpretability Challenge, a competition on distinguishing robust from spurious reasoning in frontier mathematical language models based on the models'internal mechanisms. The challenge is motivated by a central limitation of standard reasoning benchmarks: strong final-answer accuracy does not reve...

Michal vStef'anik, Philipp Mondorf, Andreas Waldis et al. · 0 citations

Preprint Aug 2026

Measuring Task-Agnostic Training Data Influence Across Language Model Pretraining

This work proposes a measure of training data influence that does not require selecting a downstream task or validation set as the attribution target, and defines an example's influence by how much its gradient update reduces the squared distance to the final parameters of a given pretraining run.

Yuto Nishida, Hirokazu Kiyomaru, Yusuke Oda et al. · 0 citations