Zhigang Wang

Diffusion language models (DLMs) have emerged as a powerful non-autoregressive alternative to GPT-style sequential generation, but suffer from substantial computational overhead due to their iterative parallel denoising. Existing acceleration works cannot accurately detect semantically stabilized...

The autoregressive inference in large language models requires repeated computation across transformer layers. While caching intermediate key-value (KV) pairs eliminates redundancy, it introduces severe memory overhead, particularly in long-context settings. Most existing cache compression method...