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

arXiv: https://arxiv.org/abs/2411.00238 · HTML: https://mlsanity.com/paper/2411.00238/

## Key facts

- Cited by 75 papers in the map (data through 14 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.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [MONet: Unsupervised Scene Decomposition and Representation](https://mlsanity.com/paper/1901.11390/) (2019)
- [On the Binding Problem in Artificial Neural Networks](https://mlsanity.com/paper/2012.05208/) (2020)
- [Compositional Attention Networks for Machine Reasoning](https://mlsanity.com/paper/1803.03067/) (2018)
- [Vision language models are blind: Failing to translate detailed visual features into words](https://mlsanity.com/paper/2407.06581/) (2024)
- [Does CLIP Bind Concepts? Probing Compositionality in Large Image Models](https://mlsanity.com/paper/2212.10537/) (2022)
- [Testing Relational Understanding in Text-Guided Image Generation](https://mlsanity.com/paper/2208.00005/) (2022)
- [Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks](https://mlsanity.com/paper/2311.09247/) (2023)
- [Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data](https://mlsanity.com/paper/2401.17600/) (2024)
- [KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models](https://mlsanity.com/paper/2407.17773/) (2024)
- [Learning to reason over visual objects](https://mlsanity.com/paper/2303.02260/) (2023)
- [GAMR: A Guided Attention Model for (visual) Reasoning](https://mlsanity.com/paper/2206.04928/) (2022)

## Built on by (most cited first, 30 of 75)

- [Video models are zero-shot learners and reasoners](https://mlsanity.com/paper/2509.20328/) (2025)
- [Grounded Reinforcement Learning for Visual Reasoning](https://mlsanity.com/paper/2505.23678/) (2025)
- [CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modally](https://mlsanity.com/paper/2502.03566/) (2025)
- [iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMs](https://mlsanity.com/paper/2502.03214/) (2025)
- [Is CLIP ideal? No. Can we fix it? Yes!](https://mlsanity.com/paper/2503.08723/) (2025)
- [Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers?](https://mlsanity.com/paper/2510.24709/) (2025)
- [LongPerceptualThoughts: Distilling System-2 Reasoning for System-1 Perception](https://mlsanity.com/paper/2504.15362/) (2025)
- [Visual symbolic mechanisms: Emergent symbol processing in vision language models](https://mlsanity.com/paper/2506.15871/) (2025)
- [Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains](https://mlsanity.com/paper/2504.20199/) (2025)
- [Do You See Me : A Multidimensional Benchmark for Evaluating Visual Perception in Multimodal LLMs](https://mlsanity.com/paper/2506.02022/) (2025)
- [Levels of Analysis for Large Language Models](https://mlsanity.com/paper/2503.13401/) (2025)
- [LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions](https://mlsanity.com/paper/2503.20252/) (2025)
- [MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation](https://mlsanity.com/paper/2411.17945/) (2024)
- [MAVERIX: Multimodal Audio-Visual Evaluation and Recognition IndeX](https://mlsanity.com/paper/2503.21699/) (2025)
- [VSC: Visual Search Compositional Text-to-Image Diffusion Model](https://mlsanity.com/paper/2505.01104/) (2025)
- [Whither symbols in the era of advanced neural networks?](https://mlsanity.com/paper/2508.05776/) (2025)
- [[De|Re]constructing VLMs' Reasoning in Counting](https://mlsanity.com/paper/2510.19555/) (2025)
- [The Geometry of Representational Failures in Vision Language Models](https://mlsanity.com/paper/2602.07025/) (2026)
- [Evaluating Compositional Scene Understanding in Multimodal Generative Models](https://mlsanity.com/paper/2503.23125/) (2025)
- [Position: We Need An Algorithmic Understanding of Generative AI](https://mlsanity.com/paper/2507.07544/) (2025)
- [Watch Before You Answer: Learning from Visually Grounded Post-Training](https://mlsanity.com/paper/2604.05117/) (2026)
- [SAFE-MEME: Structured Reasoning Framework for Robust Hate Speech Detection in Memes](https://mlsanity.com/paper/2412.20541/) (2024)
- [Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models](https://mlsanity.com/paper/2502.15678/) (2025)
- [Caption This, Reason That: VLMs Caught in the Middle](https://mlsanity.com/paper/2505.21538/) (2025)
- [Do VLMs Have Bad Eyes? Diagnosing Compositional Failures via Mechanistic Interpretability](https://mlsanity.com/paper/2508.16652/) (2025)
- [Uncovering Grounding IDs: How External Cues Shape Multimodal Binding](https://mlsanity.com/paper/2509.24072/) (2025)
- [VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes](https://mlsanity.com/paper/2509.25339/) (2025)
- [Understanding Counting Mechanisms in Large Language and Vision-Language Models](https://mlsanity.com/paper/2511.17699/) (2025)
- [The Dual Mechanisms of Spatial Variable Binding in Vision-Language Models](https://mlsanity.com/paper/2603.22278/) (2026)
- [ZeroKey: Point-Level Reasoning and Zero-Shot 3D Keypoint Detection from Large Language Models](https://mlsanity.com/paper/2412.06292/) (2024)

## 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.