The Geometry of Representational Failures in Vision Language Models
#Vision-Language Models #VLM #Representational Geometry #Binding Problem #Neural Networks #Object Recognition #AI Hallucinations
📌 Key Takeaways
- Vision-Language Models (VLMs) demonstrate significant failures in multi-object tasks, including hallucinating non-existent scene elements.
- The research draws parallels between AI errors and the 'Binding Problem' found in human cognitive psychology.
- New mechanistic insights are provided by analyzing the representational geometry within the neural networks.
- The study highlights the internal structural limitations that prevent AI from accurately identifying similar objects among distractions.
📖 Full Retelling
🏷️ Themes
Artificial Intelligence, Cognitive Science, Computer Vision
📚 Related People & Topics
Outline of object recognition
Topical guide to object recognition
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many ...
Neural network
Structure in biology and artificial intelligence
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks.
📄 Original Source Content
arXiv:2602.07025v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) exhibit puzzling failures in multi-object visual tasks, such as hallucinating non-existent elements or failing to identify the most similar objects among distractions. While these errors mirror human cognitive constraints, such as the "Binding Problem", the internal mechanisms driving them in artificial systems remain poorly understood. Here, we propose a mechanistic insight by analyzing the representational geometr