MDLMPE: Distribution Aware Positional Encoding for Masked Diffusion Language Models
Masked diffusion language models (MDLMs) enable parallel generation and bidirectional context modeling, but their positional context differs fundamentally from that of autoregressive (AR) models. Whereas AR decoding exposes a contiguous prefix, MDLM denoising produces dynamic, non-contiguous configurations of revealed and masked tokens. Conventional positional encodings such as RoPE capture sequence order and pairwise displacement but remain insensitive to this evolving token-availability structure. To address this limitation, we propose MDLMPE, a positional encoding designed specifically for masked diffusion. To the best of our knowledge, MDLMPE is the first method to make positional rep...
| Authors | Tong Ling, Hang Lei, Feng Xiao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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