Diverging Flows: Detecting Extrapolations in Conditional Generation

• URL: http://arxiv.org/abs/2602.13061v1
• Date: Fri, 13 Feb 2026 16:15:58 GMT
• Title: Diverging Flows: Detecting Extrapolations in Conditional Generation
• Authors: Constantinos Tsakonas, Serena Ivaldi, Jean-Baptiste Mouret,
• Abstract summary: Diverging Flows is a novel approach that enables a single model to simultaneously perform conditional generation and native extrapolation detection.<n>It achieves effective detection of extrapolations without compromising predictive fidelity or inference latency.<n>These results establish Diverging Flows as a robust solution for trustworthy flow models, paving the way for reliable deployment in domains such as medicine, robotics, and climate science.
• Score: 3.1784840992666137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability of Flow Matching (FM) to model complex conditional distributions has established it as the state-of-the-art for prediction tasks (e.g., robotics, weather forecasting). However, deployment in safety-critical settings is hindered by a critical extrapolation hazard: driven by smoothness biases, flow models yield plausible outputs even for off-manifold conditions, resulting in silent failures indistinguishable from valid predictions. In this work, we introduce Diverging Flows, a novel approach that enables a single model to simultaneously perform conditional generation and native extrapolation detection by structurally enforcing inefficient transport for off-manifold inputs. We evaluate our method on synthetic manifolds, cross-domain style transfer, and weather temperature forecasting, demonstrating that it achieves effective detection of extrapolations without compromising predictive fidelity or inference latency. These results establish Diverging Flows as a robust solution for trustworthy flow models, paving the way for reliable deployment in domains such as medicine, robotics, and climate science.

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