# Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systems

arXiv: https://arxiv.org/abs/2608.09964 · HTML: https://mlsanity.com/paper/2608.09964/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 63 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 22 of 22)

- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [Examining Citations of Natural Language Processing Literature](https://mlsanity.com/paper/2005.00912/) (2020)
- [Sabi'a: Portuguese Large Language Models](https://mlsanity.com/paper/2304.07880/) (2023)
- [Neural Architecture Search in Graph Neural Networks](https://mlsanity.com/paper/2008.00077/) (2020)
- [BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXams](https://mlsanity.com/paper/2307.05410/) (2023)
- [The Nature of NLP: Analyzing Contributions in NLP Papers](https://mlsanity.com/paper/2409.19505/) (2024)
- [D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research](https://mlsanity.com/paper/2204.13384/) (2022)
- [Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories](https://mlsanity.com/paper/1808.05053/) (2018)
- [The State of NLP Literature: A Diachronic Analysis of the ACL Anthology](https://mlsanity.com/paper/1911.03562/) (2019)
- [InRanker: Distilled Rankers for Zero-shot Information Retrieval](https://mlsanity.com/paper/2401.06910/) (2024)
- [ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language](https://mlsanity.com/paper/2406.10806/) (2024)
- [Sabi'a-4 Technical Report](https://mlsanity.com/paper/2603.10213/) (2026)
- [Power laws in citation distributions: Evidence from Scopus](https://mlsanity.com/paper/1402.3890/) (2014)
- [Popularity of arXiv.org within Computer Science](https://mlsanity.com/paper/1710.05225/) (2017)
- [A writer-independent approach for offline signature verification using deep convolutional neural networks features](https://mlsanity.com/paper/1807.10755/) (2018)
- [Juru: Legal Brazilian Large Language Model from Reputable Sources](https://mlsanity.com/paper/2403.18140/) (2024)
- [Authorship attribution via network motifs identification](https://mlsanity.com/paper/1607.06961/) (2016)
- [Deep learning models for representing out-of-vocabulary words](https://mlsanity.com/paper/2007.07318/) (2020)
- [What is the Impact of Releasing Code with Publications? Statistics from the Machine Learning, Robotics, and Control Communities](https://mlsanity.com/paper/2308.10008/) (2023)
- [How to Find Fantastic AI Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Review](https://mlsanity.com/paper/2510.02143/) (2025)
- [Gender Representation in Brazilian Computer Science Conferences](https://mlsanity.com/paper/2208.11020/) (2022)
- [Effects of Research Paper Promotion via ArXiv and X](https://mlsanity.com/paper/2401.11116/) (2024)

## Abstract (arXiv)

The Brazilian Conference on Intelligent Systems (BRACIS) is the main national venue for Artificial Intelligence research in Brazil, hosted by the Brazilian Computer Society since 2012 and publishing work from institutions across the country. Across eleven years, from 2015 to 2025, we build a per-paper record of all 1,066 accepted papers from DBLP metadata, 6,765 Google Scholar citations, and the paper full texts, and use it to ask what BRACIS publishes, who publishes it, and which work gets cited. Large Language Model research grows from zero before 2020 to 19% of papers in 2024, on top of a base of Machine Learning, Computer Vision, and Optimization work. The community is hourglass-shaped: 80.5% of 2,623 authors appear in a single edition, while institutions return at nearly three times the author rate. Citations are heavily concentrated, with the top 1% of papers carrying 27% of the total. Openness practices have grown, with artifact release rising from 8.9% of papers in 2015 to 57.3% in 2023, and we find a notable correlation between having an arXiv preprint and higher citation counts. Since proceedings sit behind IEEE and Springer paywalls and only 7.4% of papers have a preprint, most BRACIS work is hard to reach for readers without institutional access.