Unverified50% confidenceFactExact time
Diffusion language models (such as LLaDA and Dream) use iterative masked decoding, starting from a fully masked sequence and progressively revealing tokens over several denoising steps, with each step applying bidirectional attention over the entire sequence, so the representations of already revealed tokens are updated by newly revealed tokens.
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Long-term
Relevance
10/30/2026
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UnverifiedContinuous diffusion language models map discrete tokens to a continuous embedding space, perform the noising and denoising process in that space, and finally decode back to discrete tokens.75% similarUnverifiedAutoregressive models generate token by token from left to right, whereas diffusion models produce text in parallel through stepwise denoising of noise.74% similarUnverifiedIn bidirectional attention models (such as BERT encoders), each token's representation is affected by the entire text, so prefix KV cannot be reused independently; therefore Prompt Caching mainly appears on generative large models rather than encoder models.71% similarUnverifiedThe token encoding and decoding component is responsible for bidirectional conversion between text and token sequences and must be strictly aligned with the model vocabulary; it is a foundational link in the inference service chain that affects compatibility and correctness.71% similarUnverifiedThe decoder-only architecture uses a causal attention mask to ensure each token can only attend to its preceding context, making it suitable for autoregressive text generation tasks.70% similar
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