LLM program-search agents are usually judged by the score that drives their search.
We study LLM-driven search over Python exercise policies for Bermudan options, an optimal stopping problem where a trusted evaluator owns the simulation and a dual upper bound~$U$. For any policy, $0 \le V^\star - L_π\le U - L_π$; we report Monte Carlo estimates of both bounds with their sampling error.
We audit 24 generation attempts from two pipelines, a six-pair feedback-representation ablation, and a holdout evaluated after selection was frozen.
No selected generated program improves on Ridge LSMC consistently or by more than Monte Carlo uncertainty; holdout gap scores lie within $0.002$ of it.
On six one-dimensional puts, a high-precision binomial reference places Ridge within Monte Carlo noise of optimal and attributes 99\% of the estimated gap to dual-bound looseness, so this dual audits validity but cannot certify progress at the search's scale.
Structured memory showed no benefit over score-only memory in this ablation.
Whether search ``worked'' depends on reporting rules: retaining the initial reference, freezing selection before holdout, and counting failures.