[Submitted on 2 Oct 2026]
Title:Trust but Verify: Designing Region-Inhomogeneous Forward Dynamics for Discrete Diffusion
View a PDF of the paper titled Trust but Verify: Designing Region-Inhomogeneous Forward Dynamics for Discrete Diffusion, by Gunmay Jhingran
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Abstract:Continuous diffusion treats the forward process as a design space; discrete language diffusion often uses absorbing corruption, leaving context fully trusted throughout generation. This makes trust binary: corrupted context is copied as ground truth, propagating errors into generated spans. We reopen this design space by combining absorbing and uniform-mixing kernels with region-dependent rates. Missing positions follow absorbing dynamics, while context undergoes low-rate random substitution, exposing the model to observed but uncertain tokens during training. At inference, a heuristic sampler re-injects noise into suspicious context under an annealed intensity and freezes context updates late in the trajectory. An 8,000-step finetune of a pretrained 170M-parameter masked diffusion model takes under five hours on a 6 GB laptop GPU. The base model assigns roughly 50% probability to observed tokens even when they are uniform-random garbage. For uniform-random corruption, finetuning detects corrupted context with 0.96 recall and 0.91 precision, improves infill token-F1 by 3.8 points at 10% corruption and 6.4 points at 20%, and removes the accuracy dip next to corruptions. Clean conditioning shows no measurable regression, with a false-edit rate below 0.4%. Gains for plausible corruptions are weaker: about 0.5 F1 points at 10% corruption, without statistical significance. Our results suggest that detectability and damage depend jointly on departure from the data conditional: nearby noise is hard to detect and semantically mild, while distant noise is conspicuous and destructive. We interpret absorbing diffusion as the infinite-contrast limit of a trust axis, analogous to the signal-to-noise gap between known and unknown regions in diffusion inpainting. In our setting of a 170M model and a single text domain, intermediate trust is reached by changing forward dynamics rather than architecture.
Comments: Accepted to 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Workshop: AI for Stochastic Dynamics. 10 pages
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2610.22380 [cs.LG]
(or arXiv:2610.22380v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.22380
arXiv-issued DOI via DataCite
Submission history
From: Gunmay Jhingran Mr [view email] [v1] Fri, 2 Oct 2026 15:14:44 UTC (216 KB)
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