RAC: Rectified Flow Auto Coder

In this paper, we propose a **R**ectified Flow **A**uto **C**oder (**RAC**) inspired by Rectified Flow to replace the traditional VAE: **1.** It achieves multi-step decoding by applying the decoder to flow timesteps. Its decoding path is straight and correctable, enabling step-by-step refinement. **2.** The model inherently supports bidirectional inference, where the decoder serves as the encoder through time reversal (hence *Coder* rather than encoder or decoder), reducing parameter count by nearly 41%. **3.** This generative decoding method improves generation quality since the model can correct latent variables along the path, partially addressing the reconstruction–generation gap. Experiments show that RAC achieves a Pareto improvement over SOTA VAEs, where even a 10× parameter-reduced decoder exceeds full-scale VAE performance in both reconstruction and generation quality, validating the effectiveness of our approach.