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    <abstract>The advancement of Large Vision-Language Models (LVLMs) requires precise local region-based reasoning that faithfully grounds the model’s logic in actual visual evidence. However, existing datasets face limitations in scalability due to extensive manual annotation and lack explicit alignment between multi-step reasoning and corresponding image regions, which constrains the evaluation of model trustworthiness. To address these challenges, we propose the Visual Grounding Chain-of-Thought (VG-CoT) dataset, which explicitly links each reasoning step to real visual evidence within the image through a fully automated three-stage pipeline. The pipeline first extracts object- and text-level visual evidence using state-of-the-art detection and OCR models, then generates step-by-step grounded reasoning with GPT-4o, and finally refines the grounding through a rationale-driven open-set detection process. In addition, we introduce a new benchmark that comprehensively evaluates LVLMs reasoning across three complementary dimensions: Rationale Quality, Answer Accuracy, and Reasoning–Answer Alignment. Experiments with representative LVLMs, including LLaVA-1.5 and Qwen2-VL, demonstrate consistent improvements across all evaluation metrics, confirming that VG-CoT effectively enhances trustworthy, evidence-based reasoning while maintaining scalable and cost-efficient dataset construction. The dataset and code will be released publicly upon acceptance to facilitate further research.</abstract>
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%0 Conference Proceedings
%T VG-CoT: Towards Trustworthy Visual Reasoning via Grounded Chain-of-Thought
%A Lim, Byeonggeuk
%A Kim, Kyeonghyun
%A Yun, Jungmin
%A Kim, Youngbin
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F lim-etal-2026-vg
%X The advancement of Large Vision-Language Models (LVLMs) requires precise local region-based reasoning that faithfully grounds the model’s logic in actual visual evidence. However, existing datasets face limitations in scalability due to extensive manual annotation and lack explicit alignment between multi-step reasoning and corresponding image regions, which constrains the evaluation of model trustworthiness. To address these challenges, we propose the Visual Grounding Chain-of-Thought (VG-CoT) dataset, which explicitly links each reasoning step to real visual evidence within the image through a fully automated three-stage pipeline. The pipeline first extracts object- and text-level visual evidence using state-of-the-art detection and OCR models, then generates step-by-step grounded reasoning with GPT-4o, and finally refines the grounding through a rationale-driven open-set detection process. In addition, we introduce a new benchmark that comprehensively evaluates LVLMs reasoning across three complementary dimensions: Rationale Quality, Answer Accuracy, and Reasoning–Answer Alignment. Experiments with representative LVLMs, including LLaVA-1.5 and Qwen2-VL, demonstrate consistent improvements across all evaluation metrics, confirming that VG-CoT effectively enhances trustworthy, evidence-based reasoning while maintaining scalable and cost-efficient dataset construction. The dataset and code will be released publicly upon acceptance to facilitate further research.
%R 10.63317/28jn9v88qbod
%U https://aclanthology.org/2026.lrec-1.867/
%U https://doi.org/10.63317/28jn9v88qbod
%P 11108-11118

Markdown (Informal)

[VG-CoT: Towards Trustworthy Visual Reasoning via Grounded Chain-of-Thought](https://aclanthology.org/2026.lrec-1.867/) (Lim et al., LREC 2026)

• VG-CoT: Towards Trustworthy Visual Reasoning via Grounded Chain-of-Thought (Lim et al., LREC 2026)

ACL

• Byeonggeuk Lim, Kyeonghyun Kim, Jungmin Yun, and Youngbin Kim. 2026. VG-CoT: Towards Trustworthy Visual Reasoning via Grounded Chain-of-Thought. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 11108–11118, Palma de Mallorca, Spain. ELRA Language Resource Association.