On the spectral large sieve inequality for symmetric-squares
Pith reviewed 2026-05-24 12:09 UTC · model grok-4.3
The pith
The spectral large sieve for symmetric-squares can be improved, yet the most optimistic bound fails.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
We improve on the spectral large sieve inequality for symmetric-squares. We also prove a lower bound showing that the most optimistic upper bound is not true for this family.
What carries the argument
The family of symmetric-square L-functions attached to holomorphic cusp forms, on which the spectral large sieve is applied.
If this is right
- The improved inequality provides stronger control over averages of symmetric-square L-functions.
- The lower bound construction demonstrates that certain arithmetic correlations in the family prevent the absolute optimal bound.
- Applications of the large sieve to this family must respect the gap between the improved bound and the optimistic one.
Where Pith is reading between the lines
- The result implies that the symmetric-square family has more linear dependencies than a generic basis would suggest.
- Similar lower bounds may exist for other automorphic families with additional structure, such as higher symmetric powers.
Load-bearing premise
The symmetric-square family admits a spectral expansion allowing the large sieve to be applied directly without invalidating error terms.
What would settle it
A concrete test function for which the large sieve sum over symmetric squares exceeds the improved upper bound while staying below the dimension-based optimistic bound.
read the original abstract
We improve on the spectral large sieve inequality for symmetric-squares. We also prove a lower bound showing that the most optimistic upper bound is not true for this family.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript claims to improve the spectral large sieve inequality in the context of the symmetric-square family and to establish a lower bound showing that the most optimistic conjectural upper bound fails to hold for this family.
Significance. If the claimed improvement and the lower bound construction are rigorously proved with explicit constants and error terms, the work would contribute to the literature on large sieves for higher-rank automorphic forms by providing both a refined upper bound and evidence of sharpness for the symmetric-square case. The combination of upper and lower bounds is a standard and useful feature in this area.
minor comments (1)
- The abstract is the only visible portion of the manuscript; without access to the explicit test-function choices, error-term estimates, or the precise statement of the improved bound (including constants), it is impossible to verify whether the claimed improvement follows from the method or whether the lower bound is rigorous.
Simulated Author's Rebuttal
We thank the referee for their review and for acknowledging the potential contribution of both the refined upper bound and the matching lower bound for the symmetric-square family. The report does not list any specific major comments or points of concern.
Circularity Check
No significant circularity detected
full rationale
The paper improves the spectral large sieve inequality for the symmetric-square family and constructs an explicit lower bound showing the most optimistic upper bound fails. These results rest on standard spectral expansions and test-function techniques from the GL(2) literature, with no quoted steps reducing by definition, by fitted-parameter renaming, or by load-bearing self-citation chains to the paper's own inputs. The lower-bound construction is independent and falsifiable outside the upper-bound estimates. The derivation chain is therefore self-contained against external benchmarks.
Axiom & Free-Parameter Ledger
Reference graph
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discussion (0)
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