@inproceedings{muckatira-etal-2026-crowdmath,
title = "{CROWDMATH}: A Dataset of Crowdsourced Mathematical Research Discussions",
author = "Muckatira, Sherin and
Geneson, Jesse and
Gerovitch, Slava and
Etingof, Pavel and
Gronas, Mikhail and
Rumshisky, Anna",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1389/",
doi = "10.18653/2026.findings-emnlp.1389",
pages = "29546--29562",
ISBN = "979-8-89176-462-0",
abstract = "Large language models have made substantial progress on mathematical reasoning, but existing benchmarks typically evaluate well-specified problems with final answers, step-by-step solutions, or complete proofs. They do not capture collaborative open-problem solving: a setting in which participants propose partial arguments, identify gaps or errors in prior steps, repair flawed reasoning, and gradually synthesize incremental contributions into a proof. We introduce CrowdMath, a dataset of 164 expert-annotated progress chains from the MIT PRIMES{--}Art of Problem Solving (AoPS) CrowdMath program (2016{--}2025), a collaborative research initiative whose discussions have led to peer-reviewed publications. Each chain traces a multi-participant forum discussion from an open-problem statement to a completed proof. Posts are labeled by their functional roles in the evolving solution process, including partial progress, proof completion, erroneous reasoning, and error identification. We define evaluation tasks and benchmark six frontier models. Models achieve 83{--}88{\%} accuracy on next-post prediction, suggesting that they can follow the local flow of mathematical discussion. However, they struggle to identify the functional significance of individual contributions with the best model achieving only 0.42 macro-F1 on post-role classification. CrowdMath exposes a gap between solving well-specified mathematical problems and understanding collaborative mathematical progress as it unfolds."
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%0 Conference Proceedings
%T CROWDMATH: A Dataset of Crowdsourced Mathematical Research Discussions
%A Muckatira, Sherin
%A Geneson, Jesse
%A Gerovitch, Slava
%A Etingof, Pavel
%A Gronas, Mikhail
%A Rumshisky, Anna
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F muckatira-etal-2026-crowdmath
%X Large language models have made substantial progress on mathematical reasoning, but existing benchmarks typically evaluate well-specified problems with final answers, step-by-step solutions, or complete proofs. They do not capture collaborative open-problem solving: a setting in which participants propose partial arguments, identify gaps or errors in prior steps, repair flawed reasoning, and gradually synthesize incremental contributions into a proof. We introduce CrowdMath, a dataset of 164 expert-annotated progress chains from the MIT PRIMES–Art of Problem Solving (AoPS) CrowdMath program (2016–2025), a collaborative research initiative whose discussions have led to peer-reviewed publications. Each chain traces a multi-participant forum discussion from an open-problem statement to a completed proof. Posts are labeled by their functional roles in the evolving solution process, including partial progress, proof completion, erroneous reasoning, and error identification. We define evaluation tasks and benchmark six frontier models. Models achieve 83–88% accuracy on next-post prediction, suggesting that they can follow the local flow of mathematical discussion. However, they struggle to identify the functional significance of individual contributions with the best model achieving only 0.42 macro-F1 on post-role classification. CrowdMath exposes a gap between solving well-specified mathematical problems and understanding collaborative mathematical progress as it unfolds.
%R 10.18653/2026.findings-emnlp.1389
%U https://aclanthology.org/2026.findings-emnlp.1389/
%U https://doi.org/10.18653/2026.findings-emnlp.1389
%P 29546-29562
Markdown (Informal)
[CROWDMATH: A Dataset of Crowdsourced Mathematical Research Discussions](https://aclanthology.org/2026.findings-emnlp.1389/) (Muckatira et al., Findings 2026)
ACL