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ARXIV:2604.21396 · VISUAL REASONING · SUBMITTED 24 APR · 20:27 UTC · FRESHNESS STALE
ARXIV:2604.21396VISUAL REASONINGSUBMITTED 24 APR · 20:27 UTCFRESHNESS STALEByeonggeuk Lim · Kyeonghyun Kim · JungMin Yun · YoungBin Kim · arXiv
A dataset and benchmark for trustworthy visual reasoning that links reasoning steps to image evidence, improving LVLM performance and trustworthiness.
Opportunity summary
Pain A dataset and benchmark for trustworthy visual reasoning that links reasoning steps to image evidence, improving LVLM performance and trustworthiness.
Evidence 0 refs | 3 sources | 50% coverage
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A dataset and benchmark for trustworthy visual reasoning that links reasoning steps to image evidence, improving LVLM performance and trustworthiness. However, existing datasets face limitations in scalability due to extensive manual annotation and lack…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments with representative LVLMs, including LLaVA-1.5 and Qwen2-VL, demonstrate consistent improvements on most evaluation metrics, confirming that VG-CoT effectively enhances trustworthy, evidence-based reasoning while…
Visual Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A dataset and benchmark for trustworthy visual reasoning that links reasoning steps to image evidence, improving LVLM performance and trustworthiness.
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10.48550/arXiv.2604.21396A dataset and benchmark for trustworthy visual reasoning that links reasoning steps to image evidence, improving LVLM performance and trustworthiness.
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 of 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 on most 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.
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PROBLEM
A dataset and benchmark for trustworthy visual reasoning that links reasoning steps to image evidence, improving LVLM performance and trustworthiness. However, existing datasets face limitations in scalability due to extensive manual annotation and lack of explicit alignment bet...
METHOD
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 of ex...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments with representative LVLMs, including LLaVA-1.5 and Qwen2-VL, demonstrate consistent improvements on most evaluation metrics, confirming that VG-CoT effectively enhances trustworthy, evidence-b...
WHY NOW
Visual Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 11, "author": "Byeonggeuk Lim; Kyeonghyun Kim; JungMin Yun; YoungBin Kim", "title": "VG-CoT: Towards Trustworthy Visual Reasoning via Grounded Chain-of-Thought"
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A dataset and benchmark for trustworthy visual reasoning that links reasoning steps to image evidence, improving LVLM performance and trustworthiness.
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Visual Reasoning
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