这篇论文用实验告诉你，即使准确率一样，稀疏模型也不能直接替换密集模型——7-10%的决策会变，部署前得重新验证。
该论文研究了彩票假说（Lottery Tickets）在部署条件下的行为兼容性。实验发现，准确率匹配的稀疏候选模型（包括lottery tickets）在校准、OOD响应、类别级可靠性、表征和下游决策策略上仍与原始密集模型存在差异。在固定阈值政策诊断的小差距设置中，lottery ticket替换导致7%到10%的接受-审查决策发生变更。这表明稀疏模型无法直接替换密集模型而不重新配置下游决策逻辑，即使top-1预测完全一致也无法保证决策阈值不变。
Lottery Tickets Are Not Deployment Tickets
Reports on how sparsification, compression, and lottery tickets change model behavior have been mixed in the prior literature, with beneficial effects observed in some studies and adverse effects in others. Moreover, prior work has not considered actual deployment conditions, where decision logic is already fixed for the incumbent. To assess these mixed findings from a practical standpoint, we study the production-replacement question at the deployment level, namely whether an accuracy-matched lottery ticket or another sparse challenger can replace an incumbent dense model without reconfiguring downstream decision logic. We therefore audit a broad, protocol-specific panel of deployment-relevant behaviors spanning calibration, OOD response, class-level reliability, representations, and downstream policy decisions, and summarize clean-accuracy-excluded deviations with a behavioral-compatibility distance. Across extensive experiments, sparse candidates repeatedly recover dense-reference accuracy yet remain behaviorally different; in several study-band-matched settings, LTs also show lower corruption accuracy. In small-gap settings with fixed-threshold policy diagnostics, lottery-ticket replacement changes 7% to 10% of accept--review decisions. This churn creates precisely the burden that drop-in replacement is meant to avoid: reconfiguring and revalidating downstream decision logic. These findings establish the limits of clean-accuracy certification: Establishing compatibility with a fixed incumbent is distinct from attributing churn uniquely to sparsity or treating every measured deviation as harmful. Our theory explains the routing result: Even exact pointwise top-1 agreement cannot bound fixed-threshold decision changes, and small confidence shifts near the operating boundary can generate first-order routing churn.