# Mirror Routing

The work systematically analyzes Diffusion Transformer internal representations, finding a latent preference for early-layer feature reuse and symmetric layer pairing, and on that basis proposes a structured connectivity design that turns residual connections from passive summation into active retrieval: the DiT is split evenly along depth into an encoder and a decoder phase, encoder-side representations serve as differentiable residual sources, and each decoder sublayer adaptively fuses its mirrored encoder source through lightweight softmax routing; on ImageNet class-conditional generation, with under 0.1% additional parameters, the method lowers FID from 69.81 to 62.48 on DiT-S/2, from 44.21 to 36.33 on DiT-B/2, and from 18.85 to 15.

The work systematically analyzes Diffusion Transformer internal representations, finding a latent preference for early-layer feature reuse and symmetric layer pairing, and on that basis proposes a structured connectivity design that turns residual connections from passive summation into active retrieval: the DiT is split evenly along depth into an encoder and a decoder phase, encoder-side representations serve as differentiable residual sources, and each decoder sublayer adaptively fuses its mirrored encoder source through lightweight softmax routing; on ImageNet class-conditional generation, with under 0.1% additional parameters, the method lowers FID from 69.81 to 62.48 on DiT-S/2, from 44.21 to 36.33 on DiT-B/2, and from 18.85 to 15.