Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models

תקציר מקורי באנגליתarXiv:2607.22663v1 Announce Type: new Abstract: Block diffusion has emerged as the dominant paradigm for scaling discrete diffusion language models (dLLMs), because decoding text in fixed-size blocks preserves parallel generation within each block while keeping the quadratic attention cost tractable. However, this efficiency comes with a structural limitation: tokens near the end of a block are generated without access to future cross-block context, and once a block is finalized, its uncertain predictions become irreversible context for all subsequent blocks. This creates a block boundary problem, in which uncertainty accumulates toward block boundaries and early mistakes propagate throughout later generation. To address this issue, we propose Multi-Block Editing (MBE), to mitigate this pr