[2606.14620] Neither Parallel Nor Sequential: How DiffusionGemma Actually Commits Tokens

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Computer Science > Machine Learning

arXiv:2606.14620 (cs)

[Submitted on 12 Jun 2026]

Title:Neither Parallel Nor Sequential: How DiffusionGemma Actually Commits Tokens

Authors:Ali Asaria, Tony Salomone, Deep Gandhi<br>View a PDF of the paper titled Neither Parallel Nor Sequential: How DiffusionGemma Actually Commits Tokens, by Ali Asaria and 2 other authors

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Abstract:Open diffusion language models are marketed as parallel, non-autoregressive decoders, yet the order in which a shipped checkpoint actually commits its tokens is almost never measured. We instrument DiffusionGemma 26B, a masked discrete-diffusion mixture-of-experts model built on Gemma 4, hooking its sampler's accept step to record which canvas positions commit, when, and at what confidence. Across a 686-prompt, six-regime probe suite we find that its decoding is neither parallel nor block-autoregressive: it follows a partial left-to-right commit bias whose apparent strength depends almost entirely on the granularity at which you look. Order is weak token by token and strengthens smoothly as the analysis is coarsened, so the model's "block size" turns out to be an artifact of the measuring ruler rather than the architecture. The model commits in large simultaneous batches, leaving much of the within-batch order genuinely undefined rather than merely unobserved. The behaviour is regime-dependent: structured JSON is committed in essentially arbitrary order, and a position's commit confidence tracks correctness on mathematical reasoning but carries no signal on factual recall. Commitment is aggressive, finishing in a short late burst well inside the step budget, while task accuracy matches the model's autoregressive Gemma-4 sibling. Beyond these findings, our central contribution is methodological: measuring decoding order honestly demands handling trailing-EOS padding, within-regime confounding, commit non-monotonicity, block-size sensitivity, and large commit-batch ties, each of which can otherwise manufacture a decoding-order result that is not really there.

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Machine Learning (cs.LG)

Cite as:<br>arXiv:2606.14620 [cs.LG]

(or<br>arXiv:2606.14620v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.14620

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Deep Gandhi [view email]<br>[v1]<br>Fri, 12 Jun 2026 16:45:11 UTC (142 KB)

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