ResearchResearch paperLarge Language Models · Interpretability1 source · Oct 11, 2026

Rate-Resolved Text Evaluation with Masked Diffusion Language Models

We propose rate-resolved text evaluation, which turns the masking rate of masked diffusion language models into a diagnostic axis.

By reconstructing text under different fractions of masked tokens, one diffusion score becomes a vector of band-conditioned scores that reflect reconstruction difficulty at varying levels of visible context.
On SummEval, preserving this rate structure yields complementary signal: the best masking-rate band improves fusion with autoregressive likelihood over a collapsed diffusion scalar, survives a sample-shuffle control that breaks rate ordering, and adds residual signal to a fixed prompted judge.
On WMT MQM zh-en, roundtrip mask-and-reconstruct probing produces a tunable localization curve in which ranking-oriented AUROC and span-detection F1 peak at different masking rates.
Factuality benchmarks bound the interpretation: band semantics are model- and task-dependent, so each application requires empirical validation.

[1]Rate-Resolved Text Evaluation with Masked Diffusion Language Models
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 02:47 PM
We propose rate-resolved text evaluation, which turns the masking rate of masked diffusion language models into a diagnostic axis.
By reconstructing text under different fractions of masked tokens, one diffusion score becomes a vector of band-conditioned scores that reflect reconstruction difficulty at varying levels of visible context.

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