Problem detail · source-aware
VibeMathed reports this item as partial. VibeMath preserves that report as a source assertion and has not independently authored a plain-language mathematical explanation.
Carefully designed stepsize schedules alone accelerate plain gradient descent beyond its textbook O(1/T) rate, without momentum. Whether they can reach the optimal O(T^-2) was open. A lower bound of Omega(T^-1.9319) for last-iterate convergence under predetermined nonnegative stepsize schedules says they cannot.
The source statement is reproduced for indexing with attribution. Mathematical correctness requires domain-expert or mechanical review. VibeMath has not independently audited statement fidelity, correctness, priority, or novelty.
The abstract closes with it: "The proof was developed by GPT-5.6 Sol Pro under the authors' guidance." The authors added material to make the proof correct and readable, and separately used Codex to formalize the proof in Lean 4.
Provider: OpenAI · Prompt public: unknown · Independence: unknown
A preprint days old. A Lean 4 formalization by Codex is linked from the paper, but it is the same pipeline that produced the proof, so it is not independent confirmation.
Correctness: unknown · statement fidelity: unaudited · peer review: none
Recorded as partial: the bound is Omega(T^-1.9319) against an achievable O(T^-1.2716), so it rules out reaching the optimal rate without pinning down the true one.
Source-reported tools: argument.
Independent: unknown · difference confidence: 0
VibeMath has not independently audited the mathematical statement, proof, or novelty claim.