Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation
Discrete diffusion language models (dLLMs) recover masked tokens in parallel, offering significant speedups over autoregressive (AR) generation. However, such promising frameworks face a fundamental architectural design dilemma: \ding{182} Adopting bidirectional attention achieves strong generation quality by allowing each position to access the full context, but is inherently incompatible with KV caching, limiting inference throughput in batch-serving scenarios; \ding{183} Conversely, causal attention enables efficient cached inference but loses all right-side context, substantially degrading generation quality. This paper introduces Bifocal dLLMs, a new paradigm that resolves this dilem...
| Authors | Yuhang Chen, Xianfeng Wu, Jinhao Duan et al. |
|---|---|
| Primary category | cs.IR |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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