Topics can overlap: a paper counts once in each of its topics, but only once in the total. A topic assignment does not prove quality or reproducibility.
Steering language model generation toward desired textual properties is essential for practical deployment, and inference-time methods are particularly appealing because they enable controllable generation without retraining. Recent work has also highlighted diffusion language models as an emerging generation paradigm with distinct decoding properties. However, most existing steering approaches either rely on auxiliary models or are designed for autoregressive next-token decoding, making them difficult to apply to diffusion language models DLMs, which generate text through iterative denoising
Useful for: Not assessed · Limitation: Not assessed
Public snapshot dates
Collection recorded in this snapshot
Newest publication recorded in this snapshot
Snapshot generated
These are saved observations, not the last collector attempt or a guarantee of corpus completeness.