mlsanityData to 13 Oct 2026

Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem

arXiv 2411.00238 ↗First posted 31 Oct 2024cs.AI cs.CV cs.LGcited by 74

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

Abstract (arXiv)

Recent work has documented striking heterogeneity in the performance of state-of-the-art vision language models (VLMs), including both multimodal language models and text-to-image models. These models are able to describe and generate a diverse array of complex, naturalistic images, yet they exhibit surprising failures on basic multi-object reasoning tasks -- such as counting, localization, and simple forms of visual analogy -- that humans perform with near perfect accuracy. To better understand this puzzling pattern of successes and failures, we turn to theoretical accounts of the binding problem in cognitive science and neuroscience, a fundamental problem that arises when a shared set of representational resources must be used to represent distinct entities (e.g., to represent multiple objects in an image), necessitating the use of serial processing to avoid interference. We find that many of the puzzling failures of state-of-the-art VLMs can be explained as arising due to the binding problem, and that these failure modes are strikingly similar to the limitations exhibited by rapid, feedforward processing in the human brain.

Key facts. Cited by 74 papers in the map (data through 13 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 13 of its 40 references that are in the map. First posted on arXiv 31 Oct 2024.

Builds on

13 of 40 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models25 Jul
  2. 2024Vision language models are blind: Failing to translate detailed visual features into words9 Jul

Other references · 11

  1. 2023GPT-4 Technical Reportcited by 18,278
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,564
  3. 2019MONet: Unsupervised Scene Decomposition and Representationcited by 390
  4. 2020On the Binding Problem in Artificial Neural Networkscited by 232
  5. 2018Compositional Attention Networks for Machine Reasoningcited by 183
  6. 2022Does CLIP Bind Concepts? Probing Compositionality in Large Image Modelscited by 102
  7. 2022Testing Relational Understanding in Text-Guided Image Generationcited by 59
  8. 2023Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Taskscited by 55
  9. 2024Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation datacited by 39
  10. 2023Learning to reason over visual objectscited by 19
  11. 2022GAMR: A Guided Attention Model for (visual) Reasoningcited by 12

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 3; 2025: 36; 2026: 35.

74 papers.

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  1. 2025Video models are zero-shot learners and reasonerscited by 213
  2. 2025Grounded Reinforcement Learning for Visual Reasoningcited by 80
  3. 2025CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modallycited by 31
  4. 2025iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMscited by 25
  5. 2025Is CLIP ideal? No. Can we fix it? Yes!cited by 20
  6. 2025Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers?cited by 19
  7. 2025LongPerceptualThoughts: Distilling System-2 Reasoning for System-1 Perceptioncited by 15
  8. 2025Visual symbolic mechanisms: Emergent symbol processing in vision language modelscited by 13
  9. 2025Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chainscited by 12
  10. 2025Do You See Me : A Multidimensional Benchmark for Evaluating Visual Perception in Multimodal LLMscited by 11
  11. 2025Levels of Analysis for Large Language Modelscited by 10
  12. 2025LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questionscited by 8
38 more
  1. 2024MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creationcited by 7
  2. 2025MAVERIX: Multimodal Audio-Visual Evaluation and Recognition IndeXcited by 7
  3. 2025VSC: Visual Search Compositional Text-to-Image Diffusion Modelcited by 7
  4. 2025Whither symbols in the era of advanced neural networks?cited by 7
  5. 2026The Geometry of Representational Failures in Vision Language Modelscited by 6
  6. 2025Evaluating Compositional Scene Understanding in Multimodal Generative Modelscited by 5
  7. 2025Position: We Need An Algorithmic Understanding of Generative AIcited by 5
  8. 2025[De|Re]constructing VLMs' Reasoning in Countingcited by 5
  9. 2026Watch Before You Answer: Learning from Visually Grounded Post-Trainingcited by 5
  10. 2024SAFE-MEME: Structured Reasoning Framework for Robust Hate Speech Detection in Memescited by 4
  11. 2025Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Modelscited by 4
  12. 2025Caption This, Reason That: VLMs Caught in the Middlecited by 4
  13. 2025Do VLMs Have Bad Eyes? Diagnosing Compositional Failures via Mechanistic Interpretabilitycited by 4
  14. 2025Uncovering Grounding IDs: How External Cues Shape Multimodal Bindingcited by 4
  15. 2025VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenescited by 4
  16. 2025Understanding Counting Mechanisms in Large Language and Vision-Language Modelscited by 4
  17. 2024ZeroKey: Point-Level Reasoning and Zero-Shot 3D Keypoint Detection from Large Language Modelscited by 3
  18. 2025Visual Language Models show widespread visual deficits on neuropsychological testscited by 3
  19. 2025Bound by semanticity: universal laws governing the generalization-identification tradeoffcited by 3
  20. 2025CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environmentscited by 3
  21. 2025Sequential Enumeration in Large Language Modelscited by 3
  22. 2026The Dual Mechanisms of Spatial Variable Binding in Vision-Language Modelscited by 3
  23. 2025AI-enhanced semantic feature norms for 786 conceptscited by 2
  24. 2025Analyzing the Sensitivity of Vision Language Models in Visual Question Answeringcited by 2
  25. 2025I Spy With My Model's Eye: Visual Search as a Behavioural Test for MLLMscited by 2
  26. 2025Visual Compositional Tuningcited by 1
  27. 2025Adding simple structure at inference improves Vision-Language Compositionalitycited by 1
  28. 2026Can Vision Language Models Learn Intuitive Physics from Interaction?cited by 1
  29. 2026Visual Persuasion: What Influences Decisions of Vision-Language Models?cited by 1
  30. 2026TraversalBench: Challenging Paths to Follow for Vision Language Modelscited by 1
  31. 2026Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inferencecited by 1
  32. 2026CounterCount: A Diagnostic Framework for Counting Bias in Vision Language Modelscited by 1
  33. 2026Mechanisms of Object Localization in Vision-Language Modelscited by 1
  34. 2026Do vision-language models search like humans? Reasoning tokens as a reaction-time analog in classic visual-search paradigmscited by 1
  35. 2026Who Says What: Symbolic Trimodal Binding Mechanisms in Audio-Visual LLMscited by 1
  36. 2025Rationale-Enhanced Decoding for Multi-modal Chain-of-Thoughtcited by 0
  37. 2025Fusion to Enhance: Fusion Visual Encoder to Enhance Multimodal Language Modelcited by 0
  38. 2025Chain-of-Image Generation: Toward Monitorable and Controllable Image Generationcited by 0

50 most cited of 74 All 74 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2026Seeing to Generalize: How Visual Data Corrects Binding Shortcuts
  2. 2026Do Vision-Language Models Truly Perform Vision Reasoning? A Rigorous Study of the Modality Gap
  3. 2024UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
  4. 2025Investigating Mechanisms for In-Context Vision Language Binding
  5. 2026Vision-Language Models are Fragile Multilingual Associators
  6. 2025From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models
  7. 2026Diagnosing the Sources of Compositional Failure in Vision-Language Models: A Controlled Analysis
  8. 2026The Last Visible Pixel: Probing Fine-Scale Perception in Vision-Language Models