VG-CoT: Towards Trustworthy Visual Reasoning via Grounded Chain-of-Thought

• URL: http://arxiv.org/abs/2604.21396v1
• Date: Thu, 23 Apr 2026 08:04:07 GMT
• Title: VG-CoT: Towards Trustworthy Visual Reasoning via Grounded Chain-of-Thought
• Abstract summary: We propose the Visual Grounding Chain-of-Thought dataset, which explicitly links each reasoning step to real visual evidence within the image.<n>The pipeline generates step-by-step grounded reasoning with GPT-4o and refines the grounding through a rationale-driven open-set detection process.<n> Experiments with representative LVLMs, including LLaVA-1.5 and Qwen2-VL, demonstrate consistent improvements on most evaluation metrics.
• Score: 16.361394107862502
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• VISTAQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidence [26.0945130521806]
  Existing benchmarks assess either textual answer correctness or pixel-level localization in isolation.<n>We introduce VISTAQA, a benchmark for joint evaluation of free-form answer correctness and pixel-level evidence grounding in visual question answering.<n>We introduce GROVE, a metric that enforces joint correctness by combining textual accuracy and grounding quality via a per-sample geometric mean.
  arXiv  Detail & Related papers  (2026-05-20T03:44:06Z)
• More Than the Final Answer: Improving Visual Extraction and Logical Consistency in Vision-Language Models [74.10138874771852]
  We propose PeRL-VL (Perception and Reasoning Learning for Vision-Language Models), a decoupled framework that separately improves visual perception and textual reasoning on top of RLVR.<n>For perception, PeRL-VL introduces a VLM-based description reward that scores the model's self-generated image descriptions for faithfulness and sufficiency.<n>For reasoning, PeRL-VL adds a text-only Reasoning SFT stage on logic-rich chain-of-thought data, enhancing coherence and logical consistency independently of vision.
  arXiv  Detail & Related papers  (2025-12-13T23:06:18Z)
• Look As You Think: Unifying Reasoning and Visual Evidence Attribution for Verifiable Document RAG via Reinforcement Learning [55.232400251303794]
  Look As You Think (LAT) is a reinforcement learning framework that trains models to produce verifiable reasoning paths with consistent attribution.<n>LAT consistently improves the vanilla model in both single- and multi-image settings, yielding average gains of 8.23% in soft exact match (EM) and 47.0% in IoU@0.5.
  arXiv  Detail & Related papers  (2025-11-15T02:50:23Z)
• CIR-CoT: Towards Interpretable Composed Image Retrieval via End-to-End Chain-of-Thought Reasoning [93.05917922306196]
  Composed Image Retrieval (CIR) aims to find a target image from a reference image and a modification text.<n>CIR-CoT is the first end-to-end retrieval-oriented MLLM designed to integrate explicit Chain-of-Thought (CoT) reasoning.
  arXiv  Detail & Related papers  (2025-10-09T09:41:45Z)
• VOGUE: Guiding Exploration with Visual Uncertainty Improves Multimodal Reasoning [62.09195763860549]
  Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models (LLMs) but struggles with exploration.<n>We introduce $textbfVOGUE (Visual Uncertainty Guided Exploration)$, a novel method that shifts exploration from the output (text) to the input (visual) space.<n>Our work shows that grounding exploration in the inherent uncertainty of visual inputs is an effective strategy for improving multimodal reasoning.
  arXiv  Detail & Related papers  (2025-10-01T20:32:08Z)
• RT-VLM: Re-Thinking Vision Language Model with 4-Clues for Real-World Object Recognition Robustness [2.9979091009694088]
  Real world deployments often expose modern object recognition models to domain shifts that precipitate a severe drop in accuracy.<n>To mitigate this degradation, we introduce the Re-Thinking Vision Language Model (RT-VLM) framework.
  arXiv  Detail & Related papers  (2025-09-01T02:13:00Z)
• CoRGI: Verified Chain-of-Thought Reasoning with Post-hoc Visual Grounding [1.6257248483123767]
  We present textbfCoRGI(textbfChain textbfof textbfReasoning with textbfGrounded textbfInsights), a framework that enhances reasoning reliability through post-hoc verification of chain-of-thought outputs.
  arXiv  Detail & Related papers  (2025-08-01T07:17:12Z)
• Retrieval-Based Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios [69.00444996464662]
  We propose RIV-CoT, a Retrieval-Based Interleaved Visual Chain-of-Thought method that enables vision-language models to reason using visual crops corresponding to relevant entities.<n>Our experiments demonstrate that RIV-CoT improves answer accuracy by 3.1% and reasoning accuracy by 4.6% over vanilla CoT prompting.
  arXiv  Detail & Related papers  (2025-01-08T18:31:16Z)
• Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models [61.28463542324576]
  Vision-language models (VLMs) have recently demonstrated strong efficacy as visual assistants that can generate human-like outputs. We evaluate existing state-of-the-art VLMs and find that even the best-performing model is unable to demonstrate strong visual reasoning capabilities and consistency. We propose a two-stage training framework aimed at improving both the reasoning performance and consistency of VLMs.
  arXiv  Detail & Related papers  (2023-09-08T17:49:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.