Unverified50% confidenceFactExact time
Unlike traditional speculative decoding, SpecCTMC does not require a separate draft model and instead directly reuses the main diffusion model's own predictive distributions as the proposal distribution.
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11/6/2026
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UnverifiedSpecCTMC uses the predictive distributions already computed in previous steps of the model to continuously propose multiple token updates and evaluates the intermediate states in parallel.74% similarUnverified共形预测（Conformal Prediction）无需对数据分布做假设，直接提供具有统计保证的预测区间，在小样本场景下尤具优势68% similarUnverifiedSpecCompact only requires fine-tuning the draft model without changing the target model, giving it good practical feasibility.66% similarUnverifiedSpecCTMC is a training-free speculative sampler for reducing the sequential model-evaluation bottleneck in exact continuous-time sampling of diffusion language models.66% similarUnverifiedThe distribution-free nature of conformal prediction requires only the exchangeability assumption, meaning the order of data points does not affect their joint distribution.65% similar
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