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Remove the r<2 restriction in the operator-space LDP

Establish a large deviation principle for the covariance-process Markov chain (^2_{N_1}, …, ^{L+1}_{N_L}) on the space of non-negative, symmetric trace-class operators L_1^{+,s} under the activation growth assumption (H2) with r=2, thereby removing the current technical restriction r<2 and characterizing the corresponding good rate function.

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Background

The main operator-space LDP (Theorem \ref{thm.main}) is proven under a polynomial growth assumption on the activation function with exponent r<2 in (H2). The authors expect the result to hold for r=2 (linear growth) as well but indicate that addressing the technical difficulties is postponed.

Finite-dimensional analogues with linear-growth activations have been analyzed in recent work, suggesting feasibility; however, carrying this out in the infinite-dimensional trace-class operator setting requires overcoming significant technical hurdles.

References

We expect the same result to hold for r=2 as well, but proving this would require to deal with significant technical details (see for the finite dimensional case) and we postpone it to future work.

LDP for the covariance process in fully connected neural networks (2505.08062 - Andreis et al., 12 May 2025) in Section 3.3 (Main.thm), following Theorem \ref{thm.main}