MetaState: Persistent Working Memory Enhances Reasoning in Discrete Diffusion Language Models
Discrete diffusion language models (dLLMs) generate text by iteratively denoising a masked sequence. However, standard dLLMs condition each denoising step solely on the current hard-masked sequence, while intermediate continuous representations are discarded after sampling and remasking. We term this bottleneck the \textbf{Information Island} issue: continuous information remains isolated within individual denoising steps and fails to propagate across the trajectory. This bottleneck is especially harmful for reasoning, which requires intermediate reasoning state to be preserved and updated across many denoising steps. To address this limitation, we introduce \textbf{MetaState}, a lightwei...
| Authors | Kejing Xia, Mingzhe Li, Lixuan Wei et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 March 2026 |
| Last updated at source | 10 July 2026 |
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