@inproceedings{agarwal-etal-2025-rci,
title = "{RCI}: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks",
author = "Agarwal, Amit and
Patel, Hitesh Laxmichand and
Panda, Srikant and
Meghwani, Hansa and
Singh, Jyotika and
Dua, Karan and
Li, Paul and
Sheng, Tao and
Ravi, Sujith and
Roth, Dan",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.10/",
pages = "138--157",
ISBN = "979-8-89176-333-3",
abstract = "Multimodal Large Language Models (MLLMs) have achieved impressive results on vision-language benchmarks, yet it remains unclear whether these benchmarks assess genuine global reasoning or allow success via localized visual cues. Existing evaluation methods do not explicitly measure this distinction, hindering effective dataset curation and real-world focused model development.We introduce Region Comprehension Index (RCI), the first model-based score to directly quantify a dataset{'}s reliance on global versus local visual information. RCI systematically compares reference-model performance on image patches versus full images, revealing if tasks require holistic image understanding or can be solved with partial or localized visual cues.When applying RCI to 13 widely used multimodal benchmarks, we observed that most of them favor localized reasoning and exhibit significant spatial biases, indicating potential risks in real-world applications. RCI equips researchers {\&} practitioners with an actionable tool for diagnosing {\&} mitigating these biases, enabling the construction of datasets and benchmarks to foster the development of robust, enterprise-ready multimodal systems."
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<abstract>Multimodal Large Language Models (MLLMs) have achieved impressive results on vision-language benchmarks, yet it remains unclear whether these benchmarks assess genuine global reasoning or allow success via localized visual cues. Existing evaluation methods do not explicitly measure this distinction, hindering effective dataset curation and real-world focused model development.We introduce Region Comprehension Index (RCI), the first model-based score to directly quantify a dataset’s reliance on global versus local visual information. RCI systematically compares reference-model performance on image patches versus full images, revealing if tasks require holistic image understanding or can be solved with partial or localized visual cues.When applying RCI to 13 widely used multimodal benchmarks, we observed that most of them favor localized reasoning and exhibit significant spatial biases, indicating potential risks in real-world applications. RCI equips researchers & practitioners with an actionable tool for diagnosing & mitigating these biases, enabling the construction of datasets and benchmarks to foster the development of robust, enterprise-ready multimodal systems.</abstract>
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%0 Conference Proceedings
%T RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks
%A Agarwal, Amit
%A Patel, Hitesh Laxmichand
%A Panda, Srikant
%A Meghwani, Hansa
%A Singh, Jyotika
%A Dua, Karan
%A Li, Paul
%A Sheng, Tao
%A Ravi, Sujith
%A Roth, Dan
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F agarwal-etal-2025-rci
%X Multimodal Large Language Models (MLLMs) have achieved impressive results on vision-language benchmarks, yet it remains unclear whether these benchmarks assess genuine global reasoning or allow success via localized visual cues. Existing evaluation methods do not explicitly measure this distinction, hindering effective dataset curation and real-world focused model development.We introduce Region Comprehension Index (RCI), the first model-based score to directly quantify a dataset’s reliance on global versus local visual information. RCI systematically compares reference-model performance on image patches versus full images, revealing if tasks require holistic image understanding or can be solved with partial or localized visual cues.When applying RCI to 13 widely used multimodal benchmarks, we observed that most of them favor localized reasoning and exhibit significant spatial biases, indicating potential risks in real-world applications. RCI equips researchers & practitioners with an actionable tool for diagnosing & mitigating these biases, enabling the construction of datasets and benchmarks to foster the development of robust, enterprise-ready multimodal systems.
%U https://aclanthology.org/2025.emnlp-industry.10/
%P 138-157
Markdown (Informal)
[RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks](https://aclanthology.org/2025.emnlp-industry.10/) (Agarwal et al., EMNLP 2025)
ACL
- Amit Agarwal, Hitesh Laxmichand Patel, Srikant Panda, Hansa Meghwani, Jyotika Singh, Karan Dua, Paul Li, Tao Sheng, Sujith Ravi, and Dan Roth. 2025. RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 138–157, Suzhou (China). Association for Computational Linguistics.