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arxiv: 2604.21396 · v1 · submitted 2026-04-23 · 💻 cs.CV · cs.AI

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

Byeonggeuk Lim , Kyeonghyun Kim , JungMin Yun , YoungBin Kim This is my paper

Pith reviewed 2026-05-09 22:30 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords Visual GroundingChain-of-Thought ReasoningLarge Vision-Language ModelsDataset ConstructionReasoning AlignmentAutomated AnnotationTrustworthy AI
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The pith

VG-CoT dataset explicitly links each reasoning step in visual chain-of-thought to specific image regions via an automated pipeline.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes VG-CoT to solve the problem of ungrounded and unscalable reasoning datasets for large vision-language models. It builds a dataset that connects every step of a model's reasoning directly to real objects or text detected in the image. The construction uses a three-stage automated process: first extracting visual evidence with detection and OCR tools, then generating step-by-step reasoning with GPT-4o, and finally refining the links with rationale-driven detection. A new benchmark measures rationale quality, answer accuracy, and how well reasoning aligns with the final answer. Tests on models such as LLaVA-1.5 and Qwen2-VL show gains on most of these measures when using the dataset.

Core claim

The VG-CoT dataset, built through a fully automated pipeline of visual evidence extraction, GPT-4o-generated grounded reasoning, and rationale-driven open-set detection, produces explicit alignments between each multi-step reasoning step and corresponding image regions, enabling large vision-language models to achieve higher scores on rationale quality, answer accuracy, and reasoning-answer alignment in a new evaluation benchmark.

What carries the argument

The VG-CoT dataset and its three-stage automated pipeline that extracts object- and text-level evidence, generates step-by-step reasoning, and refines region alignments.

If this is right

  • Large vision-language models evaluated or trained with VG-CoT produce more evidence-aligned reasoning steps.
  • The automated construction method scales dataset size without requiring large amounts of manual annotation.
  • The three-dimensional benchmark reveals gaps in reasoning-answer alignment that prior metrics missed.
  • Models show measurable gains in trustworthiness metrics while keeping dataset creation cost-efficient.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same grounding technique could be adapted to trace errors in model outputs back to specific visual misalignments during inference.
  • If the alignments hold, VG-CoT-style data might support training loops that penalize ungrounded reasoning steps directly.
  • The pipeline's reliance on existing detectors suggests it could extend to new image domains once those detectors improve.

Load-bearing premise

The automated pipeline using detection models, GPT-4o, and open-set detection produces accurate groundings that faithfully match visual evidence without inheriting errors or hallucinations.

What would settle it

Manual inspection of a random sample of generated reasoning steps revealing frequent mismatches between the stated rationale and the linked image regions or objects.

Figures

Figures reproduced from arXiv: 2604.21396 by Byeonggeuk Lim, JungMin Yun, Kyeonghyun Kim, YoungBin Kim.

Figure 1
Figure 1. Figure 1: Examples of the Proposed VG-CoT Dataset across Three Task Types. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the Automated Pipeline for Generating Grounded CoT Data. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Detailed Statistics of the VG-CoT Dataset: Bounding Box Size Distribution (R: Relative Area [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper introduces the VG-CoT dataset for visual grounding chain-of-thought reasoning in LVLMs. It is built via a fully automated three-stage pipeline (object/text detection and OCR, GPT-4o step-by-step reasoning generation, and rationale-driven open-set detection) that explicitly links each reasoning step to image regions. The work also defines a new benchmark with three dimensions (Rationale Quality, Answer Accuracy, Reasoning-Answer Alignment) and reports consistent improvements on representative models including LLaVA-1.5 and Qwen2-VL.

Significance. If the pipeline outputs are shown to be faithful, VG-CoT would offer a scalable, low-cost alternative to manual annotation for datasets that support evaluation of evidence-based visual reasoning, directly addressing current limitations in trustworthiness assessment for LVLMs.

major comments (1)
  1. The manuscript describes the three-stage automated pipeline but provides no quantitative validation of the resulting VG-CoT dataset (e.g., human audit of grounding accuracy, error rates on rationales or region links, or inter-annotator agreement). This is load-bearing: the central claim that VG-CoT produces trustworthy, evidence-based reasoning and yields meaningful benchmark improvements rests on the assumption that the outputs faithfully reflect visual evidence without inheriting hallucinations from GPT-4o or mis-detections from the OCR/detection stages. Absent such verification, the reported gains on LLaVA-1.5 and Qwen2-VL cannot be confidently interpreted as evidence of increased trustworthiness.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We appreciate the referee's thorough review and constructive criticism. We respond to the major comment point-by-point below and outline the revisions we will make to address the concerns raised.

read point-by-point responses

  1. Referee: [—] The manuscript describes the three-stage automated pipeline but provides no quantitative validation of the resulting VG-CoT dataset (e.g., human audit of grounding accuracy, error rates on rationales or region links, or inter-annotator agreement). This is load-bearing: the central claim that VG-CoT produces trustworthy, evidence-based reasoning and yields meaningful benchmark improvements rests on the assumption that the outputs faithfully reflect visual evidence without inheriting hallucinations from GPT-4o or mis-detections from the OCR/detection stages. Absent such verification, the reported gains on LLaVA-1.5 and Qwen2-VL cannot be confidently interpreted as evidence of increased trustworthiness.

    Authors: We thank the referee for this important observation. The VG-CoT pipeline is constructed to minimize ungrounded reasoning by first extracting visual evidence using established detection and OCR models, then generating reasoning steps explicitly conditioned on this evidence via GPT-4o, and finally refining the region associations with rationale-driven open-set detection. Nevertheless, we concur that without quantitative human validation, it is difficult to fully assess the faithfulness of the dataset and the reliability of the reported improvements. To address this, we will perform a human audit on a sampled portion of the VG-CoT dataset. Specifically, we will measure the accuracy of the initial detections, the alignment of generated rationales with the visual evidence (to detect potential hallucinations), and the precision of the final grounding links. We will report error rates, inter-annotator agreement, and incorporate these findings into the revised manuscript, including a new subsection on dataset validation. This will provide the necessary evidence to support our claims regarding trustworthy visual reasoning. revision: yes

Circularity Check

0 steps flagged

No circularity detected in derivation or claims

full rationale

The paper introduces a new VG-CoT dataset via a three-stage automated pipeline (detection/OCR, GPT-4o reasoning, rationale-driven detection) and a new three-dimensional benchmark, then reports empirical improvements on off-the-shelf LVLMs such as LLaVA-1.5 and Qwen2-VL. No equations, fitted parameters, or predictions are defined in terms of the target results; no self-citations are invoked as load-bearing uniqueness theorems; and no ansatz or renaming reduces the central claims to inputs by construction. The derivation chain consists of an empirical construction and evaluation that remains independent of its own outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the assumption that existing detection, OCR, and GPT-4o models can be composed into a faithful grounding pipeline; no new free parameters, axioms, or invented entities are introduced.

pith-pipeline@v0.9.0 · 5537 in / 1102 out tokens · 19860 ms · 2026-05-09T22:30:21.245264+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · 4 internal anchors

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    Introduction The development of Large Language Models (LLMs) has recently led to the rise of Large Vision- Language Models (LVLMs), which simultaneously understand visual and linguistic information (Zhang et al., 2024; Yin et al., 2024). LVLMs have demon- stratedoutstandingperformanceincomprehensive, image-level understanding, and the focus of re- search ...

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    We propose the VG-CoT dataset, which ex- plicitly aligns the reasoning process with real visual evidence within the image through an automated three-stage pipeline

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    We establish a new benchmark that compre- hensively measures Rationale Quality and Reasoning–Answer Alignment, going beyond simple answer accuracy

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    We evaluate representative LVLMs using VG- CoTtoanalyzetheircapabilityinutilizingvisual evidence, providing insights into the future di- rection of LVLMs research

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    JOVIAL CAR[0.070,0.405,0.296,0.483]

    Related Work 2.1. Datasets for LVLMs Reasoning Early datasets for LVLMs primarily focused on image-wide understanding, which limited their abil- ity to train models for fine-grained, local region- basedreasoning(Linetal.,2014;Antoletal.,2015; Hudson and Manning, 2019). To overcome this lim- itation, datasets that explicitly include local region informatio...

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    VG-CoT Dataset Toovercomethelimitationsofexistingdatasets,this study introduces the Visual Grounding Chain-of- Thought (VG-CoT) dataset. The constructionof VG- CoTisdrivenbythreecoreobjectives: (1)achieving scalability through a fully automated pipeline, (2) securing trustworthy reasoning by explicitly linking each logical step to precise visual evidence ...

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    Additionally, for the GQA dataset, the rich scene graph information provided by the dataset itself is leveraged as supplementary initial evidence

    extracts the location and textual content within the image, robustly handling complex environments. Additionally, for the GQA dataset, the rich scene graph information provided by the dataset itself is leveraged as supplementary initial evidence. Stage 2: Visually-Grounded CoT Generation. The second stage bridges the gap between visual perception and logi...

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    Explain what makes the answer correct by comparing or contrast- ing with what is NOT true. ... Rationale: Table 1: Prompt Template for Generating step-by- step Rationales for VQA. the path to the correct answer but also to analyze why certain elements are incorrect. This requires the model to demonstrate the plausibility of its an- swer through comparison...

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    Experiment In this section, we conduct experiments to validate the effectiveness of the proposed VG-CoT dataset and demonstrate the utility of our new benchmark. 4.1. Experimental Setup Evaluated LVLMs.To validate the effectiveness of our proposed dataset, we perform fine-tuning and evaluation on four representative LVLMs: LLaVA- 1.5 (7B and 13B) (Liu et ...

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    Conclusion This study aimed to address the limitations of ex- isting datasets and benchmarks to enhance the trustworthy rationale-based reasoning capabilities of LVLMs. To this end, we proposed the VG-CoT dataset and an automated three-stage pipeline for its construction, enabling the scalable and reliable generation of visual evidence-based CoT data. Ad-...

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    Limitations Although this study contributes to enhancing visual evidence-based reasoning in LVLMs, it has certain limitations that suggest directions for future work. First, as the proposed VG-CoT framework is a fully automated three-stage pipeline designed for scalability, its performance is inherently tied to the integration of its underlying foundation...

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    The proposed automated pipeline does not involve human annotation or data collection from individ- uals, thereby minimizing ethical risks

    Ethics Statement This study was conducted using publicly available datasets (GQA, Visual7W, TextVQA) that contain no personally identifiable or sensitive information. The proposed automated pipeline does not involve human annotation or data collection from individ- uals, thereby minimizing ethical risks. All experi- ments and analyses were performed in co...

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