@inproceedings{sanchez-etal-2026-iol,
title = "The {IOL}-{AI} Challenge: An Open Challenge towards Advancing Linguistic Reasoning",
author = "S{\'a}nchez, Eduardo and
Berrada, Rita and
Mirea, Dan-Mircea and
Rajaee, Sara and
Piperski, Alexander and
Dolinar, Ana Meta and
Iomdin, Boris and
Nikulin, Andrey and
Shmatova, Mariya and
Fadaee, Marzieh and
Kreutzer, Julia",
editor = "Ranaldi, Leonardo and
Pucci, Giulia and
Ranaldi, Federico and
Valentino, Marco and
Birch, Alexandra and
Zanzotto, Fabio Massimo",
booktitle = "The Proceedings of the Workshop on Open Reasoning Across Cultures Languages ({ORACLE})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.oracle-main.30/",
pages = "433--458",
ISBN = "979-8-89176-483-5",
abstract = "Reasoning in LLMs is overwhelmingly studied in domains that provide a model with rules: mathematics and code. Linguistic puzzles invert this: the solver must first discover the system before reasoning within it. We present the IOL-AI Challenge, an open-science competition run on the unseen problems of the International Linguistics Olympiad (IOL) 2026 Individual Contest, evaluated both automatically and, for the first time, by members of the official IOL Jury under the same rubrics applied to human contestants. The challenge drew 731 submissions from 46 teams under a strict compute budget (one T4, 30 mins). We additionally benchmark 18 unconstrained frontier and open models, with Claude Opus 4.8 earning a jury score equivalent to a gold medal, while both resource-constrained systems we submitted for jury grading scored in the range of the bottom 5{\%} of contestants. Capability was not determined by scale: 14B submissions outperform models twice their size, and gains come from decoding and output-handling rather than model capacity. We also found that automatic metrics rank systems exactly as the jury does, but compress the scale, upscoring weak systems by {\ensuremath{\sim}}13 points and understating strong ones. Our analysis shows that while frontier models might have prior knowledge about some of the problem languages, it does not significantly help them solve the linguistic reasoning tasks, leaving linguistic reasoning as a strong benchmarking proxy for generalizable reasoning skills."
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<abstract>Reasoning in LLMs is overwhelmingly studied in domains that provide a model with rules: mathematics and code. Linguistic puzzles invert this: the solver must first discover the system before reasoning within it. We present the IOL-AI Challenge, an open-science competition run on the unseen problems of the International Linguistics Olympiad (IOL) 2026 Individual Contest, evaluated both automatically and, for the first time, by members of the official IOL Jury under the same rubrics applied to human contestants. The challenge drew 731 submissions from 46 teams under a strict compute budget (one T4, 30 mins). We additionally benchmark 18 unconstrained frontier and open models, with Claude Opus 4.8 earning a jury score equivalent to a gold medal, while both resource-constrained systems we submitted for jury grading scored in the range of the bottom 5% of contestants. Capability was not determined by scale: 14B submissions outperform models twice their size, and gains come from decoding and output-handling rather than model capacity. We also found that automatic metrics rank systems exactly as the jury does, but compress the scale, upscoring weak systems by \ensuremath\sim13 points and understating strong ones. Our analysis shows that while frontier models might have prior knowledge about some of the problem languages, it does not significantly help them solve the linguistic reasoning tasks, leaving linguistic reasoning as a strong benchmarking proxy for generalizable reasoning skills.</abstract>
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%0 Conference Proceedings
%T The IOL-AI Challenge: An Open Challenge towards Advancing Linguistic Reasoning
%A Sánchez, Eduardo
%A Berrada, Rita
%A Mirea, Dan-Mircea
%A Rajaee, Sara
%A Piperski, Alexander
%A Dolinar, Ana Meta
%A Iomdin, Boris
%A Nikulin, Andrey
%A Shmatova, Mariya
%A Fadaee, Marzieh
%A Kreutzer, Julia
%Y Ranaldi, Leonardo
%Y Pucci, Giulia
%Y Ranaldi, Federico
%Y Valentino, Marco
%Y Birch, Alexandra
%Y Zanzotto, Fabio Massimo
%S The Proceedings of the Workshop on Open Reasoning Across Cultures Languages (ORACLE)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-483-5
%F sanchez-etal-2026-iol
%X Reasoning in LLMs is overwhelmingly studied in domains that provide a model with rules: mathematics and code. Linguistic puzzles invert this: the solver must first discover the system before reasoning within it. We present the IOL-AI Challenge, an open-science competition run on the unseen problems of the International Linguistics Olympiad (IOL) 2026 Individual Contest, evaluated both automatically and, for the first time, by members of the official IOL Jury under the same rubrics applied to human contestants. The challenge drew 731 submissions from 46 teams under a strict compute budget (one T4, 30 mins). We additionally benchmark 18 unconstrained frontier and open models, with Claude Opus 4.8 earning a jury score equivalent to a gold medal, while both resource-constrained systems we submitted for jury grading scored in the range of the bottom 5% of contestants. Capability was not determined by scale: 14B submissions outperform models twice their size, and gains come from decoding and output-handling rather than model capacity. We also found that automatic metrics rank systems exactly as the jury does, but compress the scale, upscoring weak systems by \ensuremath\sim13 points and understating strong ones. Our analysis shows that while frontier models might have prior knowledge about some of the problem languages, it does not significantly help them solve the linguistic reasoning tasks, leaving linguistic reasoning as a strong benchmarking proxy for generalizable reasoning skills.
%U https://aclanthology.org/2026.oracle-main.30/
%P 433-458
Markdown (Informal)
[The IOL-AI Challenge: An Open Challenge towards Advancing Linguistic Reasoning](https://aclanthology.org/2026.oracle-main.30/) (Sánchez et al., ORACLE 2026)
ACL
- Eduardo Sánchez, Rita Berrada, Dan-Mircea Mirea, Sara Rajaee, Alexander Piperski, Ana Meta Dolinar, Boris Iomdin, Andrey Nikulin, Mariya Shmatova, Marzieh Fadaee, and Julia Kreutzer. 2026. The IOL-AI Challenge: An Open Challenge towards Advancing Linguistic Reasoning. In The Proceedings of the Workshop on Open Reasoning Across Cultures Languages (ORACLE), pages 433–458, Budapest, Hungary. Association for Computational Linguistics.