Key points
- Contiguous, non-interleaved blocks can create the appearance of sequential generation despite different within-block token orders.
- A rank-correlation identity separates the schedule's contribution from the average correlation inside individual blocks.
- The reported paired evaluation used LLaDA-8B-Instruct with 500 mathematics and coding prompts.
Context
The work explores why generation-order measurements can be misleading in masked diffusion language models that process text in blocks. It differentiates order imposed by the overall schedule from randomness or reversal within each block, helping evaluators pinpoint the true source of sequential behavior.
What this means for you
Directly applicable
You can use schedule-aware correlation analysis to distinguish processing artifacts from genuinely sequential generation when evaluating masked diffusion language models.
Who benefits: Developers and technical decision makers who evaluate or integrate masked diffusion language models gain a clearer way to interpret generation behavior.
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