Embedding Inversion via Conditional Masked Diffusion Language Models

Han Xiao

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arXiv · 2602.11047

Embedding Inversion via Conditional Masked Diffusion Language Models

Han Xiao

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Abstract

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.

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Han Xiao. 2026-02-11. Embedding Inversion via Conditional Masked Diffusion Language Models. https://arxiv.org/abs/2602.11047

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