Adaptive Energy-Guided Routing for Robust Out-of-Distribution Detection with Synthetic In-Distribution Data

Out-of-Distribution (OOD) detection under adversarial attacks is critical for safety-sensitive AI systems. Prevailing OOD-centric methods face a fundamental limit—the OOD space is infinite—while the in-distribution (ID) manifold is compact, suggesting a complementary strategy: enriching the model's ID representation with synthetic data from diffusion models. However, the effectiveness of synthetic ID data is tied to its generation quality: high-fidelity synthetic samples effectively improve robust OOD detection, while low-quality samples can cause severe degradation when naively incorporated. To enable robust use of synthetic ID data across varying quality levels, we propose Adaptive Energy-Guided Routing (AEGR). AEGR uses a teacher classifier's LogSumExp energy as a per-sample fidelity oracle, computing a dynamic weight $\gamma$ that controls a dual-objective routing mechanism: high-quality samples ($\gamma \approx 0$) undergo standard classification, while low-quality samples ($\gamma \approx 1$) are steered toward a margin-regularized uniform loss that flattens overconfident predictions and penalizes excessive energy. We show that the explicit margin is structurally justified by a gradient zero-sum property of the uniform loss; the reject-class paradigm avoids this requirement through its implicit energy suppression. Experiments across four datasets demonstrate that AEGR consistently improves robust OOD detection, with the largest recovery over naive synthetic data usage on the most challenging dataset where generation quality is lowest, while preserving gains on simpler datasets and relaxing the margin when generation quality is sufficient.