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[2602.05319] Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective
Summary
This paper introduces Accelerated Sequential Flow Matching, a Bayesian filtering framework that enhances real-time inference in stochastic dynamical systems by reducing sampling latency.
Why It Matters
The research addresses a critical challenge in machine learning related to real-time prediction from streaming data. By improving the efficiency of flow-based models, it has implications for various applications in forecasting and decision-making, making it relevant for both academic research and practical implementations in AI.
Key Takeaways
- Sequential Flow Matching provides a new framework for real-time inference.
- The method significantly reduces sampling latency compared to traditional approaches.
- It leverages Bayesian filtering principles for improved predictive performance.
- The approach is competitive with full-step diffusion models while requiring fewer sampling steps.
- Code for the method is publicly available, promoting further research and application.
Computer Science > Machine Learning arXiv:2602.05319 (cs) [Submitted on 5 Feb 2026 (v1), last revised 15 Feb 2026 (this version, v2)] Title:Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective Authors:Yinan Huang, Hans Hao-Hsun Hsu, Junran Wang, Bo Dai, Pan Li View a PDF of the paper titled Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective, by Yinan Huang and 4 other authors View PDF HTML (experimental) Abstract:Sequential prediction from streaming observations is a fundamental problem in stochastic dynamical systems, where inherent uncertainty often leads to multiple plausible futures. While diffusion and flow-matching models are capable of modeling complex, multi-modal trajectories, their deployment in real-time streaming environments typically relies on repeated sampling from a non-informative initial distribution, incurring substantial inference latency and potential system backlogs. In this work, we introduce Sequential Flow Matching, a principled framework grounded in Bayesian filtering. By treating streaming inference as learning a probability flow that transports the predictive distribution from one time step to the next, our approach naturally aligns with the recursive structure of Bayesian belief updates. We provide theoretical justification that initializing generation from the previous posterior offers a principled warm start that can accelerate sampling compared to naïve re-sampling. Across a wide range of forecasting, de...
