# MioFFAn: an Annotation Software for Formula Formalization with LLM Automation Capabilities

arXiv: https://arxiv.org/abs/2607.22552 · HTML: https://mlsanity.com/paper/2607.22552/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 15 May 2026.

## Builds on (most cited first, 11 of 11)

- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics](https://mlsanity.com/paper/2109.00110/) (2021)
- [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023)
- [Solving Math Word Problems by Combining Language Models With Symbolic Solvers](https://mlsanity.com/paper/2304.09102/) (2023)
- [PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs](https://mlsanity.com/paper/2502.00963/) (2025)
- [Autoformalization in the Wild: Assessing LLMs on Real-World Mathematical Definitions](https://mlsanity.com/paper/2502.12065/) (2025)
- [Autoformalization in the Era of Large Language Models: A Survey](https://mlsanity.com/paper/2505.23486/) (2025)
- [Neural Machine Translation: A Review of Methods, Resources, and Tools](https://mlsanity.com/paper/2012.15515/) (2020)
- [Neural Machine Translation for Mathematical Formulae](https://mlsanity.com/paper/2305.16433/) (2023)
- [Improving the Representation and Conversion of Mathematical Formulae by Considering their Textual Context](https://mlsanity.com/paper/1804.04956/) (2018)
- [Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics](https://mlsanity.com/paper/2408.10003/) (2024)

## Abstract (arXiv)

The automatic translation of mathematical expressions in scientific literature into executable symbolic code (a process we refer to as Formula Formalization) is hindered by a severe scarcity of high-quality, ground-truth datasets specialized for technical scientific domains. In this paper, we present MioFFAn, an open-source, document-centric, and customizable framework designed to facilitate rapid annotation for this task. Building upon the MioGatto architecture, we extend existing features to overcome structural limitations and pivot its scope by introducing specific functionalities for Formula Formalization, such as selection of equations of interest and aided symbolic code specification. By allowing users to configure custom taxonomies and properties for identified symbols, and compatible symbolic operators, we ensure the framework is adaptable to diverse specialized scientific fields. Furthermore, MioFFAn is designed to incorporate partial automation via Large Language Models. By defining a modular set of automated sub-tasks with strict output formats, we enable researchers to iteratively refine automation capabilities and evaluate competing strategies using standard NLP metrics. We specify the current automation methodology and perform a preliminary evaluation that demonstrates to efficacy of this human-in-the-loop approach.