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arXiv AI By Yukai Jin, Zhankai Ye, Jiayue Yang, Fangyi Li, Shuoqiu Li, Xu Gao, Shibo Li, Xin Liu, Shangqian Gao

TraceQuery: learning what to recompute from full-denoising trajectory in diffusion language models

Read the original on arXiv AI →

TraceQuery proposes a new approach for efficient inference in diffusion large language models (dLLMs) by guiding computation using the full-denoising trajectory. Instead of broadly repairing representation errors, it predicts a small set of near-future trajectory-relevant positions, queries only those, and lets the backbone choose the best prediction. This lightweight sparse decoding framework achieves a superior quality‑computation trade‑off across tasks, model variants, and families.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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