# A Markovian View of Iterative-Feedback Loops in Image Generative Models: Neural Resonance and Model Collapse Source: [A Markovian View of Iterative-Feedback Loops in Image Generative Models: Neural Resonance and Model Collapse]() Collection index: [llms.txt]() Data updated: 2026-02-25T05:00:03.907Z Authors: Vibhas Kumar Vats, David J. Crandall, Samuel Goree Published: 2026-02-22T04:05:04.000Z ## Abstract AI training datasets will inevitably contain AI-generated examples, leading to \`\`feedback'' in which the output of one model impacts the training of another. It is known that such iterative feedback can lead to model collapse, yet the mechanisms underlying this degeneration remain poorly understood. Here we show that a broad class of feedback processes converges to a low-dimensional invariant structure in latent space, a phenomenon we call neural resonance. By modeling iterative feedback as a Markov Chain, we show that two conditions are needed for this resonance to occur: ergodicity of the feedback process and directional contraction of the latent representation. By studying diffusion models on MNIST and ImageNet, as well as CycleGAN and an audio feedback experiment, we map how local and global manifold geometry evolve, and we introduce an eight-pattern taxonomy of collapse behaviors. Neural resonance provides a unified explanation for long-term degenerate behavior in generative models and provides practical diagnostics for identifying, characterizing, and eventually mitigating collapse. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()