Small-SNR no-outlier regime and monotonicity in SNR
Sourced from the work of Gerard Ben Arous, Reza Gheissari, Jiaoyang Huang, Aukosh Jagannath
§ Problem Statement
Setup
Fix integers , mixture weights with , a class map , and an aspect ratio . For each , let class means , collect , and let parameters . For SNR parameter , data are generated by
with observed class label . Define and the summary Gram matrix
Call feasible if for some (equivalently, and realizable in some ambient dimension).
Fix a class index . Let for , and for define
For , let be the effective bulk law with Stieltjes transform solving
Define by
Effective outliers are real roots of ; define the right-outlier set
Unsolved Problem
For fixed feasible , characterize the dependence of right-outlier existence on . In particular, determine whether
and analogously with in place of . Also determine whether there exists such that
§ Discussion
§ Significance & Implications
Without this, the transition structure can be pathologically non-monotone, making global interpretation of SNR-dependent geometry unclear. A monotonicity/no-outlier-at-low-SNR theorem would complete the phase-transition narrative for this effective spectral framework. See Arous et al. (2025) for details.
§ Known Partial Results
Arous et al. (2025): The paper proves large- outlier existence but explicitly notes that a complementary small- no-outlier theorem is missing and that non-monotone scenarios are not ruled out. The problem is open as of January 22, 2026 (arXiv v3).
§ References
Gerard Ben Arous, Reza Gheissari, Jiaoyang Huang, Aukosh Jagannath (2025)
Annals of Statistics (to appear)
📍 Section 1.3.2 ("The effective spectrum at initialization and along the training trajectory"), immediately after Corollary 1.8 (comment on possible non-monotonicity and missing small-$\lambda$ no-outlier result), p. 12 (arXiv v3).
Source paper where this problem appears (first posted in 2025; this entry cites arXiv v3 from 2026).