PeopleofSuperintelligence

Researcher

Caio César Teodoro Mendes

Also known as Caio C. T. Mendes, Caio CT Mendes

Caio César Teodoro Mendes is a researcher whose work spans mobile robotics, autonomous-vehicle vision, and language models. In 2012, he presented a dissertation on mobile-robot navigation with stereo vision; he coauthored a paper on contextual-block road detection in 2015. His later work examined compact autocomplete models and Microsoft's Phi-1, Phi-2, Phi-3, Phi-4, and Phi-4-reasoning. As of October 2026, he contributes to MagenticBrain, one of Microsoft Research's research releases for agentic experiences optimized for small models.

Works
26
Citations
809
h-index
10

Publication numbers from OpenAlex, as of 19 October 2026.

Timeline

  1. 2012

    Mobile robot navigation

    In 2012, he presents a dissertation on stereo-vision navigation for mobile robots.

  2. 2015–2017

    Navigability for autonomous vehicles

    Contextual blocks feature in his 2015 work on monocular road detection. His 2017 thesis applies machine learning to navigability estimation for autonomous vehicles.

  3. 2022

    Smaller autocomplete models

    A 2022 study he coauthors shows that a 20-million-parameter character model achieves accuracy similar to an 80-million-parameter word model on autocomplete.

  4. 2023

    Phi-1 and Phi-2

    Textbooks Are All You Need introduces phi-1 for code in 2023, and he coauthors the paper. He also contributes to Microsoft Research's 2023 Phi-2 small-language-model release.

  5. 2024

    Phi-3 and Phi-4 reports

    In 2024, he coauthors the Phi-3 technical report and the Phi-4 report, which introduces a 14-billion-parameter language model trained with a recipe focused on data quality.

  6. 2025

    Phi-4 reasoning

    The 2025 Phi-4-reasoning report introduces a 14-billion-parameter model for complex reasoning tasks, and he coauthors it.

  7. As of October 2026

    MagenticBrain for small models

    In 2026, he contributes to MagenticBrain, one of Microsoft Research's research releases for agentic experiences optimized for small models.

Most-cited works

  1. Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone2024 · 164 citationsWith Adil Salim, Ahmed Awadallah, Ce Liu, Ronen Eldan, Shital Shah and Sébastien Bubeck
  2. Textbooks Are All You Need2023 · 138 citationsWith Adam Tauman Kalai, Adil Salim, Ronen Eldan, Shital Shah, Sébastien Bubeck and Yin Tat Lee
  3. Corn Plant Counting Using Deep Learning and UAV Images2019 · IEEE Geoscience and Remote Sensing Letters · 135 citations
  4. Exploiting fully convolutional neural networks for fast road detection2016 · IEEE International Conference on Robotics and Automation · 123 citations
  5. CaRINA Intelligent Robotic Car: Architectural design and applications2014 · Journal of Systems Architecture · 106 citations

From OpenAlex, as of 19 October 2026.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Universidade de São Paulo. Our estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.
  2. The company share of AI researchers’ top papers rose from 2% to 32%Includes Universidade de São Paulo. We estimate where leading AI researchers worked when their most-cited papers appeared. The company share rose from 2% in 1975–1979 to 32% in 2020–2024.
  3. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  4. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Metadados do item: Navegação de robôs móveis utilizando visão estéreo bdtd.ibict.br. Checked 18 October 2026.
  2. Vision-Based Road Detection using Contextual Blocks arxiv.org. Checked 18 October 2026.
  3. Metadados do item: Navigability estimation for autonomous vehicles using machine learning bdtd.ibict.br. Checked 18 October 2026.
  4. Small Character Models Match Large Word Models for Autocomplete Under Memory Constraints arxiv.org. Checked 18 October 2026.
  5. Textbooks Are All You Need arxiv.org. Checked 18 October 2026.
  6. Phi-2: The surprising power of small language models microsoft.com. Checked 18 October 2026.
  7. Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone microsoft.com. Checked 18 October 2026.
  8. Phi-4 Technical Report microsoft.com. Checked 18 October 2026.
  9. Phi-4-reasoning Technical Report microsoft.com. Checked 18 October 2026.
  10. MagenticLite, MagenticBrain, Fara1.5: An agentic experience optimized for small models microsoft.com. Checked 18 October 2026.