A new method called Conflict-Aware Additive Guidance (g^car) has been developed to improve the performance of diffusion and flow models by addressing issues with off-manifold drift during controlled generation. This approach enhances generation fidelity while requiring minimal computational resources, making it a promising advancement in AI model training and application.