# Benchmarking Arabic Dialects for Math Problems

Authors: Noor Abo Mokh, Teresa Lynn, Nizar Habash, Ahmed Elshabrawy, Sara Alhajeri, Alaa Elsetohy, Preslav Nakov, Alham Fikri Aji.

Introduces parallel translated and culturally localized math problems for Modern Standard Arabic and five dialects. Uses residual-state comparisons and activation patching to study how localization changes internal processing and answer accuracy.

First public: 2026-10-20.
ArXiv initial submission: 2026-10-20.

[Original paper](https://arxiv.org/abs/2610.28607) · [PDF](https://arxiv.org/pdf/2610.28607) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.28607/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.28607/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.28607/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.28607/record.json)

## Approaches

- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

Reading-list version 0.11.0; literature checked through 2026-10-26.