SCARCE: Scalable Cascade Analysis for Rare-event Characterisation via Embeddings
Rare events govern the safety profile of modern AI systems, yet their probabilities are extremely difficult to estimate: direct Monte Carlo requires prohibitive sample budgets. Subset Simulation (SS) addresses this by decomposing a rare-event probability into moderate conditional probabilities over nested intermediate events. However, classical SS requires a handcrafted scalar performance function whose sublevel sets define those events, demanding detailed knowledge of the failure geometry and limiting transfer to new domains. We propose SCARCE (Scalable Cascade Analysis for Rare-event Characterisation via Embeddings), which replaces the performance function with learned latent representa...
| Authors | Yingjie Wang, Yi Dong, Edmund Lau et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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