Investigating Relational Reasoning in VLMs

• URL: http://arxiv.org/abs/2608.23518v1
• Date: Mon, 24 Aug 2026 17:23:36 GMT
• Title: Investigating Relational Reasoning in VLMs
• Abstract summary: Vision-Language Models (VLMs) achieve strong performance in visual reasoning tasks.<n>It remains unclear whether they understand visual relations, or simply employ shortcuts such as language cues or priors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) achieve strong performance in visual reasoning tasks, but it remains unclear whether they understand visual relations, or simply employ shortcuts such as language cues or priors. To investigate this, we use the Qwen3-VL-4B (Bai et al., 2025), a modern VLM, to decode how visual information is encoded across depths. For this, we propose a synthetic dataset of simple geometric shapes for controlled analysis, along with queries crafted to precisely test language cues. Furthermore, the dataset is modified to test causal reliance on visual evidence. Our results show that current VLMs combine genuine visual reasoning with shortcut strategies primarily rooted in language cues.

Related papers

• Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities [54.94982467313341]
  Vision-language Models (VLMs) have emerged as general-purpose tools for addressing a variety of complex computer vision problems.<n>We set out to understand the limitations of SoTA VLMs on fundamental visual tasks by constructing a series of tests that probe which components of design, specifically, may be lacking.
  arXiv  Detail & Related papers  (2025-07-10T15:26:41Z)
• Revisit What You See: Disclose Language Prior in Vision Tokens for LVLM Decoding [6.612630497074871]
  Large Vision-Language Models (LVLMs) achieve strong performance across multimodal tasks by integrating visual perception with language understanding.<n>We propose ReVisiT, a training-free decoding method that references vision tokens to guide text generation.
  arXiv  Detail & Related papers  (2025-06-11T08:46:55Z)
• Hidden in plain sight: VLMs overlook their visual representations [48.83628674170634]
  We compare vision language models (VLMs) to their visual encoders to understand their ability to integrate across these modalities.<n>We find that VLMs perform substantially worse than their visual encoders, dropping to near-chance performance.
  arXiv  Detail & Related papers  (2025-06-09T17:59:54Z)
• Re-Aligning Language to Visual Objects with an Agentic Workflow [73.73778652260911]
  Language-based object detection aims to align visual objects with language expressions.<n>Recent studies leverage vision-language models (VLMs) to automatically generate human-like expressions for visual objects.<n>We propose an agentic workflow controlled by an LLM to re-align language to visual objects via adaptively adjusting image and text prompts.
  arXiv  Detail & Related papers  (2025-03-30T16:41:12Z)
• Where do Large Vision-Language Models Look at when Answering Questions? [35.39354978511109]
  Large Vision-Language Models (LVLMs) have shown promising performance in vision-language understanding and reasoning tasks.<n>We extend existing heatmap visualization methods to support LVLMs for open-ended visual question answering.<n>We conduct a comprehensive analysis of state-of-the-art LVLMs on benchmarks designed to require visual information to answer.
  arXiv  Detail & Related papers  (2025-03-18T04:34:43Z)
• FiVL: A Framework for Improved Vision-Language Alignment through the Lens of Training, Evaluation and Explainability [10.184567639685321]
  We introduce FiVL, a novel method for constructing datasets designed to train LVLMs for enhanced visual grounding.<n>We present benchmarks to assess the model's ability to use image as substantive evidence.<n>We identify attention heads with the strongest vision-language alignment, enabling explainability on visual-driven hallucinations.
  arXiv  Detail & Related papers  (2024-12-19T09:24:10Z)
• Distill Visual Chart Reasoning Ability from LLMs to MLLMs [64.32993770646165]
  Solving complex chart Q&A tasks requires advanced visual reasoning abilities in multimodal large language models (MLLMs)<n>We propose Code-as-Intermediary Translation (CIT), a cost-effective, efficient and scalable data synthesis method for distilling visual reasoning abilities from LLMs to MLLMs.<n>ReachQA is a dataset containing 3k reasoning-intensive charts and 20k Q&A pairs to enhance both recognition and reasoning abilities of MLLMs.
  arXiv  Detail & Related papers  (2024-10-24T14:50:42Z)
• Improving Visual Commonsense in Language Models via Multiple Image Generation [41.565399860320966]
  Existing large language models (LLMs) are primarily trained using textual data only. Visual Language Models, which excel at visually-oriented tasks, often fail at non-visual tasks such as basic commonsense reasoning. This divergence highlights a critical challenge - the integration of robust visual understanding with foundational text-based language reasoning.
  arXiv  Detail & Related papers  (2024-06-19T15:17:10Z)
• Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning [59.13366859237086]
  Current solutions for efficiently constructing large vision-language (VL) models follow a two-step paradigm. We consider visual prompts as additional knowledge that facilitates language models in addressing tasks associated with visual information. We introduce a novel approach, wherein visual prompts are memoryd with the weights of FFN for visual knowledge injection.
  arXiv  Detail & Related papers  (2024-05-09T08:23:20Z)
• See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning [60.43585179885355]
  We propose a novel framework named Interactive Prompting Visual Reasoner (IPVR) for few-shot knowledge-based visual reasoning. IPVR contains three stages, see, think and confirm. We conduct experiments on a range of knowledge-based visual reasoning datasets.
  arXiv  Detail & Related papers  (2023-01-12T18:59:50Z)
• Visually-augmented pretrained language models for NLP tasks without images [77.74849855049523]
  Existing solutions often rely on explicit images for visual knowledge augmentation. We propose a novel textbfVisually-textbfAugmented fine-tuning approach. Our approach can consistently improve the performance of BERT, RoBERTa, BART, and T5 at different scales.
  arXiv  Detail & Related papers  (2022-12-15T16:13:25Z)

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.