Diffusion language models generate text by iteratively denoising all tokens in parallel, but when and where their hidden states encode whether the output will be functionally correct remains unknown.We present the first probing study of DLM internals, training linear classifiers on hidden states to predict functional correctness.Across two models (LLaDA-8B, Dream-7B) and four tasks, we find that DLMs uniquely accumulate correctness signal across denoising steps (AUC gains of 0.08–0.11 on reasoning tasks), absent in single-pass AR decoding. However, step-0 signal reflects prompt difficulty rather than diffusion-specific computation. Signal emergence is task-dependent: structural tasks show flat profiles while reasoning tasks show gradual buildup. The two models exhibit distinct layer dynamics, with LLaDA concentrating signal in upper layers while Dream redistributes toward lower layers. We further show that probe confidence can identify likely failures, enabling selective generation that avoids 36–98% of wasted compute.

Diffusion language models (DLMs) decode several tokens per step. However, the grammar-constrained decoders evaluated here, IG-CD and LAVE, verify candidate tokens sequentially within each denoising step and treat every violation as a rejection, a lookahead verification, or a fallback to autoregressive decoding. We observe that a violation is local to the first token the parser cannot consume, so the rest of the parallel step can be kept. We propose Dgrammar, a training-free decoder that repairs that frontier token in place under the same forward-pass logits, paired with a grammar-gated backoff schedule for the unmask batch size, asynchronous mask construction, and a bounded grammar-guided tail. On JSONSchemaBench medium with LLaDA-8B, Dgrammar improves schema validity by 8.6 percentage points over the strongest DLM baseline while reducing mean latency by 5.8x and eliminating timeouts. Validity and latency gains also hold on the other JSB splits and on Dream-7B, and experiments with a SMILES grammar demonstrate cross-grammar transfer.