{"ID":6536246,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10810","arxiv_id":"2607.10810","title":"Diachronic Sample Integration: Robust Tail-Risk Estimation with Generative Models","abstract":"Deep generative models are increasingly used as simulators for downstream decision-making under data scarcity, but in risk-sensitive applications their usefulness depends on rare adverse scenarios rather than typical samples. Standard generative objectives prioritize bulk distributional fidelity, leaving low-probability tails vulnerable to localized optimization noise and making tail-dependent functionals unstable under finite simulation budgets. We introduce Diachronic Sample Integration (DSI), a test-time inference framework that ensembles generated samples across checkpoints from a stochastic training trajectory. DSI targets a checkpoint-mixture distribution that averages checkpoint-specific tail fluctuations rather than relying on a single brittle endpoint. We formalize this mechanism through a finite-budget bias-variance theory. Empirically, across multivariate synthetic processes and high-frequency trading data, DSI substantially reduces tail-estimation error compared to single-checkpoint baselines under fixed simulation budgets, outperforming standard diffusion and state-of-the-art tail-aware baselines without modifying the generative objective.","short_abstract":"Deep generative models are increasingly used as simulators for downstream decision-making under data scarcity, but in risk-sensitive applications their usefulness depends on rare adverse scenarios rather than typical samples. Standard generative objectives prioritize bulk distributional fidelity, leaving low-probabilit...","url_abs":"https://arxiv.org/abs/2607.10810","url_pdf":"https://arxiv.org/pdf/2607.10810v1","authors":"[\"Shuning Zhao\",\"Patrick Wong\",\"Leran Zhang\",\"Xiaolin Hu\"]","published":"2026-07-12T15:45:23Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"q-fin.RM\"]","methods":"[\"Diffusion Model\"]","has_code":false}