What was already known
The shifted approximation problem fixes a real shift and asks for an almost-everywhere divergence criterion. Removing restrictions on that shift is separate from the homogeneous problem.
What changed and why it matters
Resolves the weak inhomogeneous divergence target for every fixed shift, including irrational shifts.
The AI contribution
OpenAI attributes these manuscripts to its internal model. The entry does not infer individual human contributions beyond that publisher attribution.
Limitations
Numerators are unrestricted rather than required coprime. This is the weak inhomogeneous divergence statement, not a convergence converse or a quantitative counting asymptotic. Preprint. This is a source-statement and significance assessment, not a proof audit. No independent checking or successful formal compilation is asserted by this entry.
Verification evidence
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Author claim
The publisher reports the scoped result. AIProofWatch has not independently validated its correctness.
Accessible artifact / proof
The original written argument is accessible. Availability does not establish a successful proof check or Lean verification.
Correction and publication history
Historical report added after statement, scope, and importance review. No proof audit or formal checking asserted; subscriber alerts disabled.
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