TAPS: Target-Aware Prefix Tree Selection for Diffusion-Drafted Speculative Decoding

• URL: http://arxiv.org/abs/2606.00487v1
• Date: Sat, 30 May 2026 02:39:40 GMT
• Title: TAPS: Target-Aware Prefix Tree Selection for Diffusion-Drafted Speculative Decoding
• Abstract summary: Using a diffusion model for parallel drafting is a promising approach for speculative decoding.<n>This shifts the bottleneck to verification: verifying a single sequence limits acceptance length, while verifying large draft trees incurs excessive target-model latency.<n>We propose TAPS, a target-aware prefix selection method that turns diffusion marginals into path-conditioned acceptance estimates.
• Score: 4.5881678566958985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Using a diffusion model for parallel drafting is a promising approach for speculative decoding. By predicting tokens at multiple future positions in a single forward pass, diffusion drafters substantially reduce drafting latency. However, this shifts the bottleneck to verification: verifying a single sequence limits acceptance length, while verifying large draft trees incurs excessive target-model latency. We identify a key mismatch in existing draft-tree methods: existing diffusion-tree methods rank nodes by the marginal probability, ignoring that verification is prefix-conditioned. As a result, they may verify unreachable descendants of rejected prefixes, increasing latency with limited acceptance gains. To address this, we propose TAPS, a target-aware prefix selection method that turns diffusion marginals into path-conditioned acceptance estimates. TAPS then selects a compact prefix-closed subtree under a fixed verification budget, improving the acceptance-cost tradeoff rather than simply expanding the draft tree. Experiments across diverse datasets and model families demonstrate that TAPS achieves up to 7.9x lossless end-to-end speedup over vanilla autoregressive decoding, outperforming state-of-the-art DFlash and DDTree by 1.36x and 1.74x respectively. Our work is available at https://anonymous.4open.science/r/TAPS-EMNLP2026-53DD

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