# End-to-End Guarantees for Masked Diffusion Models under Conditional Mixing

Authors: Fareed Sheriff.

Derives learning and decoding-error bounds for masked diffusion under an assumption that distant tokens have rapidly decreasing influence. Studies how spacing out tokens revealed together trades parallelism against dependence, and identifies limits of the assumption for natural language.

First public: 2026-10-29.
ArXiv initial submission: 2026-10-29.

[Original paper](https://arxiv.org/abs/2610.39444) · [PDF](https://arxiv.org/pdf/2610.39444) · [HTML](https://arxiv.org/html/2610.39444v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.39444/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.39444/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.39444/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.39444/record.json)

## Approaches

- [Alternative generation methods](https://latent-implicit-thinking.github.io/topics/alternative-reasoning-schedules/): Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. Some produce written chains of thought.
- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

Reading-list version 0.12.0; literature checked through 2026-11-02.