Vision models do not look for objects so much as they use destructive interference to cancel out everything else.
The spatial funnel hypothesis suggests that neural networks progressively filter features until only the object remains. New adjoint inversion techniques reveal that the process is actually more like wave physics. Weights create a negative image of the background noise to isolate the primary subject. This means the model identifies a cat by actively deleting the grass and the sky from its perception. Understanding this holographic mechanism could lead to much more efficient computer vision architectures that operate on interference rather than subtraction. It changes the fundamental way we teach machines to see the world.
Adjoint Inversion Reveals Holographic Superposition and Destructive Interference in CNN Classifiers
arXiv · 2604.27529
A foundational assumption in CNN interpretability -- that deep encoders suppress background pixels while classifiers merely select from a cleaned feature pool (the Spatial Funnel Hypothesis) -- remains untested due to spatial hallucinations in existing visualization tools. We address this by introducing a hallucination-free inversion framework built on magnitude-phase decoupling and Local Adjoint Correctors. Our method mathematically guarantees that the spatial gradient support of every reconstr