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The lab behind LLaDA2.2 released a diffusion model benchmarked against its own autoregressive model, showing diffusion lags on general knowledge and coding but wins on speed and agent tasks, providing a clean tradeoff data point.
This paper reinterprets masked diffusion language model decoding as continuous clean-state prediction, introducing a flow-based framework where tokens are updated continuously and asynchronously based on confidence, achieving 97% of LLaDA's performance with 25% of the decoding budget.