Mode Collapse Is Cheap to Detect: A Ground-Truth-Free Pre-Flight Check for Neural Samplers
Neural samplers are trained against an unnormalised target $\tildeπ=e^{-E}$ with no samples from $π$, which leaves the practitioner with no way to tell whether an expensive training run has silently dropped part of the target. The diagnostics in common use are computed from the model's own draws and are therefore confined to the model's support: we exhibit a sampler whose self-normalised effective sample size is $0.99$ while it misses $87\%$ of the target mass. We argue that \emph{detecting} missing mass is a strictly easier problem than sampling it: detection needs one point per missed basin plus a local curvature estimate, whereas correction needs the sampler retrained. We turn this int...
| Authors | Jian Xu |
|---|---|
| Primary category | cs.LG |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.