这篇论文严格证明了时序图生成中分布漂移无法通过观测数据来校正，理论扎实且有实验支撑，做生成模型部署的朋友值得一看。
论文将masked flow-matching loss分解为不可约熵加散度，散度对训练时稀疏部署时密集的结构为正，经验上服从指数-0.605的幂律（R²=0.9977）。漂移使采样误差floor提高2.2-34.3倍，但采样步数不敏感：7个条件下漂移时段边际误差变化≤6%。证明任何基于过去观测的校正器至少保留跟踪统计量的条件方差，趋势外推条件μ²>v(1-2ρ)不满足。Oracle可移除60%误差，最佳观测校正仅恢复其中5.7%，外推比什么都不做更差。
When Can You Correct Distribution Drift in Temporal Graph Generation? A Sharpening--Drift Tension and an Impossibility for Observation-Based Correction
Generative models of temporal graphs are trained on one stretch of an evolving network and deployed on the next, and they degrade badly in the gap. We show this degradation is derivable, general, and not fixable from observations. The masked flow-matching loss decomposes exactly, with no independence assumption, into an irreducible entropy plus a divergence whose derivative along the training path is positive precisely for structures rare during training and common at deployment, diverging as their training probability goes to zero. Empirically the trade-off is a power law with exponent $-0.605$ ($R^2=0.9977$), and drift raises the sampler's error floor without changing how many steps reach it: across seven well-powered conditions the drift-period marginal error varies by at most $6\%$ over a $50\times$ range of sampling budgets, while the floor sits $2.2\times$ to $34.3\times$ above the in-period floor. Because the deployment period is observed, correction looks like a matter of measurement. It is not. We prove that any corrector measurable with respect to past observations leaves at least the conditional variance of the statistic it tracks, and that trend extrapolation beats trusting the last observation only when $μ^2>v(1-2ρ)$. Both premises are measurable and both go the wrong way: the drift is trendless and mean-reverting, with a one-step innovation as large as the drift itself. An oracle removes $60\%$ of the error, the best observation-based corrector recovers $5.7\%$ of that, and extrapolation is strictly worse than doing nothing clever.