Backbone-Equated Diffusion OOD via Sparse Internal Snapshots

Authors: Yadang Alexis Rouzoumka, Jean Pinsolle, Eug'enie Terreaux et al.

arXiv · PDF

Summary

The authors tackle fair comparison in diffusion-based out-of-distribution (OOD) detection by introducing a standardized evaluation protocol (MBE: Mutualized Backbone-Equated) that aligns corruption levels and test-time cost across different diffusion backbones. They then propose Canonical Feature Snapshots (CFS), a family of OOD detectors that probe a frozen diffusion model using only a tiny number of internal activations at low-noise timesteps, without running full denoising trajectories. The strongest one-forward variant (CFS(1x2)) and an even smaller decoder-only variant remain highly competitive, showing that most of the OOD signal in diffusion models is concentrated in sparse internal states rather than requiring expensive full sampling.

Main takeaways:

• MBE protocol standardizes comparisons across diffusion OOD methods by aligning corruption levels and test-time compute budgets
• CFS detectors extract OOD signals from a frozen diffusion backbone using only a few internal activations at low-noise timesteps, not full denoising
• The strongest one-forward variant (CFS(1x2)) and a decoder-only variant are competitive, showing OOD information is concentrated in sparse internal states
• Local diagnostic theory explains this through conditional encoder-decoder complementarity, diagonal-score separation, and low-noise stability

Relevance

Not directly related to my persona/midtraining work—this is about OOD detection in diffusion models. Tangentially relevant in that it probes internal activations of frozen models to extract signal, similar in spirit to how I might probe LLM internals for persona-related representations, but the application domain is completely different.

Threat model

Potential threat/caveat for clean result "Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)": this item discusses benchmark.

Abstract

arXiv:2605.11014v1 Announce Type: new Abstract: Fair comparison between diffusion-based OOD detectors is challenging, as conclusions can vary with backbone choice, corruption parameterization, and test-time budget. We address this issue through a Mutualized Backbone-Equated (MBE) protocol that aligns canonical corruption levels and logical test-time cost across diffusion backbones. Within this setting, we introduce Canonical Feature Snapshots (CFS), a family of detectors that probes a frozen diffusion backbone using only a tiny number of native internal activations at canonical low-noise levels. On a controlled CIFAR-scale benchmark, the strongest one-forward CFS variant is CFS(1x2), while an even smaller decoder-only variant remains highly competitive. This shows that much of the relative-OOD signal exposed by frozen diffusion backbones is concentrated in a small number of sparse internal states, rather than requiring full denoising trajectories or high-capacity downstream heads. We further provide a local diagnostic theory explaining these observations through conditional encoder-decoder complementarity, diagonal-score separation, and low-noise corruption stability. The official implementation is available at https://github.com/RouzAY/cfs-diffusion-ood/.