mlsanityData to 7 Oct 2026

Nodes Are Early, Edges Are Late: Probing Diagram Representations in Large Vision-Language Models

arXiv 2603.02865 ↗First posted 3 Mar 2026cs.CL cs.CVcited by 0

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

Large vision-language models (LVLMs) demonstrate strong performance on diagram understanding benchmarks, yet they still struggle with understanding relationships between elements, particularly those represented by nodes and directed edges (e.g., arrows and lines). To investigate the underlying causes of this limitation, we probe the internal representation of LVLMs using a carefully constructed synthetic diagram dataset based on directed graphs. Our probing experiments reveal that edge information is not linearly separable in the vision encoder and becomes linearly encoded only in the text tokens in the language model. In contrast, node information and global structural features are already linearly encoded in individual hidden states of the vision encoder. These findings suggest that the stage at which linearly separable representations are formed varies depending on the type of visual information. In particular, the delayed emergence of edge representations may help explain why LVLMs struggle with relational understanding, such as interpreting edge directions, which require more abstract, compositionally integrated processes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 30 of its 42 references that are in the map. First posted on arXiv 3 Mar 2026.

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30 of 42 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning18 Dec
  2. 2024Do Vision-Language Models Really Understand Visual Language?30 Sep
  3. 2024MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?21 Mar
  4. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts3 Oct

Other references · 26

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  4. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  5. 2016Understanding intermediate layers using linear classifier probescited by 1,279
  6. 2016A Diagram Is Worth A Dozen Imagescited by 793
  7. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 733
  8. 2018Annotation Artifacts in Natural Language Inference Datacited by 656
  9. 2019Designing and Interpreting Probes with Control Taskscited by 608
  10. 2018What you can cram into a single vector: Probing sentence embeddings for linguistic propertiescited by 452
  11. 2019What do you learn from context? Probing for sentence structure in contextualized word representationscited by 436
  12. 2022Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Taskcited by 417
14 more
  1. 2023Finding Neurons in a Haystack: Case Studies with Sparse Probingcited by 328
  2. 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 328
  3. 2023Interpreting CLIP's Image Representation via Text-Based Decompositioncited by 189
  4. 2017An Analysis of Visual Question Answering Algorithmscited by 90
  5. 2021SciCap: Generating Captions for Scientific Figurescited by 76
  6. 2024DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMscited by 72
  7. 2021Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answeringcited by 71
  8. 2023Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?cited by 62
  9. 2024FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understandingcited by 26
  10. 2023Localizing Lying in Llama: Understanding Instructed Dishonesty on True-False Questions Through Prompting, Probing, and Patchingcited by 24
  11. 2023Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarizationcited by 14
  12. 2024Evaluating Nuanced Bias in Large Language Model Free Response Answerscited by 2
  13. 2024Shape It Up: An Empirically Grounded Approach for Designing Shape Palettescited by 2
  14. 2023Perception of Line Attributes for Visualizationcited by 1

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