# PACT: Certifying What Is Written in Parallel Decoding of Diffusion Language Models

Authors: Yanchao Li, Yusu Zhang, Jiaqing Xie, Yi Ban, Ben Gao, Zhongyu Huang, Tianfan Fu, Yuqiang Li.

Samples candidate tokens before choosing which positions to commit in parallel. Uses the probabilities of the actual sampled tokens, plus spacing between positions, to control the reliability of each committed set.

First public: 2026-11-03.
ArXiv initial submission: 2026-11-03.

[Original paper](https://arxiv.org/abs/2611.04152) · [PDF](https://arxiv.org/pdf/2611.04152) · [HTML](https://arxiv.org/html/2611.04152v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2611.04152/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2611.04152/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2611.04152/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2611.04152/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 0.15.2; literature checked through 2026-11-03.