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Rao Muhammad Anwer

Rao Muhammad Anwer has 3 papers in our library, in 2025–2026, most of them on Vision & language, Reasoning, Semitic languages; often with Hisham Cholakkal, Salman Khan, Sara Ghaboura.

3 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 35 · 176 works · 5,048 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 55 citations in our library · 5 more in our library without a page yet · ⭐ Paper Star rank #4755

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Mohamed bin Zayed University of Artificial IntelligenceAbu Dhabi · United Arab Emirates2020–2026 · 118 paperscurrent🎓Aalto UniversityEspoo · Finland2014–2026 · 34 paperscurrent🔬Inception Institute of Artificial IntelligenceAbu Dhabi · United Arab Emirates2019–2021 · 11 papers🎓Universitat Autònoma de BarcelonaCerdanyola del Vallès · Spain2008–2013 · 5 papers🎓Linköping UniversityLinköping · Sweden2023–2024 · 2 papers🔬Computer Vision CenterCerdanyola del Vallès2011–2012 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Topics

 Vision & language (3) Reasoning (2) Semitic languages (2) Language models & pretraining (1) Evaluation & benchmarks (1)

 Works by year

2020s

20262 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AIN: The Arabic INclusive Large Multimodal Model (2025 · 11 citations)
    Arabic text-only LLMs have improved, but Arabic multimodal models still cover only narrow slices of vision-language understanding.
  2. ARB: A Comprehensive Arabic Multimodal Reasoning Benchmark (2026 · 3 citations)
    Benchmarks that check how multimodal models reason step by step, and not only whether the final answer is right, exist almost entirely in English.
  3. AgriChain: Visually-Grounded Expert-Verified Reasoning for Interpretable Agricultural Vision–Language Models (2026)
    Vision–language models (VLMs) can often name a plant disease but rarely explain the diagnosis. They are also weakly grounded in agronomic imagery and tend to be overconfident. Existing agricultural resources such as PlantVillage, AgroInstruct and AgroMind provide labels or QA pairs but no step-by-step rationales.

Also in our library

5 more works of theirs are in our library without a page here yet; they will appear as we publish them.

 Co-authors here

4 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 55 times (of the 140,884 papers whose bibliographies we read); 10 citing papers with a page here →.

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