arXiv:2511.17812v3 Announce Type: replace-cross Abstract: Flow matching models effectively represent complex distributions, yet estimating expectations of functions of their outputs remains challenging under limited sampling budgets. Independent sampling often yields high-variance estimates, especially when rare but high-impact outcomes dominate the expectation. We propose a non-IID sampling framework that jointly draws multiple samples to cover diverse, salient regions of a flow matching model's generative distribution. To balance diversity and quality, we introduce a score-based regularizati
The paper addresses a core challenge in flow matching models as they become more sophisticated and widely adopted, requiring robust sampling techniques for accurate estimations, particularly for rare events.
Improved sampling methods for generative models can significantly enhance the reliability and efficiency of AI systems, leading to more accurate predictions and better decision-making in high-stakes applications.
This research introduces a method for more efficient and robust expectation estimation in complex generative models, moving beyond independent sampling to capture diverse, high-impact outcomes.
- · AI researchers and developers
- · Industries relying on complex AI simulations
- · Generative AI applications with high-impact rare events
- · Inefficient AI sampling methods
More accurate and efficient deployment of flow matching models in various AI applications.
Accelerated development of AI agents and decision-making systems that rely on understanding complex distributions and rare events.
Potentially enables new classes of AI applications that require high fidelity in estimating extreme or unusual outcomes.
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Read at arXiv cs.AI