We frame embedding inversion as conditional masked diffusion, recovering all tokens in parallel through iterative denoising rather than sequential autoregressive generation. A masked diffusion language model is conditioned on the target embedding via adaptive layer normalization, requiring only 8 forward passes with no access to the target encoder at inference time. On 32-token sequences across three embedding models, the method achieves token recovery through parallel denoising without requiring encoder access, iterative correction, or architecture-specific alignment. Source code and live demo are available at https://github.com/jina-ai/embedding-inversion-demo.

翻译：我们将嵌入反演问题构建为条件掩码扩散过程，通过迭代去噪而非顺序自回归生成的方式并行恢复所有词元。该方法通过自适应层归一化将掩码扩散语言模型以目标嵌入为条件，在推理时仅需8次前向传播且无需访问目标编码器。在三种嵌入模型的32词元序列上，该方法通过并行去噪实现词元恢复，无需编码器访问、迭代修正或特定架构对齐。源代码与实时演示详见 https://github.com/jina-ai/embedding-inversion-demo。