arXiv Machine Learning By Franziska Weindel, Michael Girsch, Reinhard Heckel

Trace Reconstruction with Language Models

Read the original on arXiv Machine Learning →

arXiv:2507. 12927v2 Announce Type: replace Abstract: The general trace reconstruction problem seeks to recover an original sequence from its noisy copies independently corrupted by insertions, deletions, and substitutions.

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arXiv AI
3d ago

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

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.

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