---
title: "DecoCal — paper by Xu et al. (2026) — summary and ideas"
description: "This creates a choice that autoregressive decoding avoids: at every step the decoder must pick which positions to fix and… 33 references."
authors: ["Fan Xu", "Huixuan Zhang", "Xiaojun Wan"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.545/"
doi: "10.18653/v1/2026.acl-long.545"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2026-decocal-decoding-calibration-diffusion-large/"
---
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# DecoCal: Decoding with Calibration in Diffusion Large Language Models

**Authors:** [Fan Xu](https://md.research.elamur.ai/free/en/authors/fan-xu/), [Huixuan Zhang](https://md.research.elamur.ai/free/en/authors/huixuan-zhang/) and [Xiaojun Wan](https://md.research.elamur.ai/free/en/authors/xiaojun-wan/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.545/) · [PDF](https://aclanthology.org/2026.acl-long.545.pdf) · [DOI 10.18653/v1/2026.acl-long.545](https://doi.org/10.18653/v1/2026.acl-long.545) · [OpenAlex](https://openalex.org/W7166854544)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Diffusion large language models (DLLMs) such as LLaDA and Dream write text by repeatedly filling in masked tokens. This creates a choice that autoregressive decoding avoids: at every step the decoder must pick which positions to fix and whether to undo earlier picks. Common policies rely only on the current step's probabilities. Those probabilities often swing between steps or are inflated early, so errors get locked in. The authors propose DecoCal, an inference-time, model-agnostic decoding scheme that keeps a running, smoothed distribution for every position. Each new prediction is blended with this running estimate in log space, and the blending weight depends on how far the two diverge (KL). A token is revealed only once it passes a confidence threshold and a consistency check. Tokens that later appear premature are masked again, with a cool-down and a limit on repeated remasking, and a fallback guarantees progress. On several DLLMs and benchmarks, DecoCal improves accuracy over existing decoding strategies in most cases. This suggests that tracking confidence over time is important for diffusion-based generation.

## Key points
- Calibration rule: weight w_t = σ(γ·KL(p_t ‖ q_{t−1})), and the new estimate q_t is a softmax over a w_t-weighted mix of log q_{t−1} and log p_t (a geometric aggregation).
- The method has three stages: calibration, unmasking gated by both confidence and consistency checks, and remasking with a cool-down. Code is released at github.com/pku0xff/DecoCal.
- Four hyperparameters must be set: the confidence cutoff A, the KL scaling factor γ, K (the cap on how many times a token may be remasked), and the cool-down length C. These need tuning per task.
- Total steps are capped at T·(K+1), but the method offers no exact per-sample step or latency control.
- In a GSM8K case study with LLaDA-1.5, decoding without calibration committed the wrong answer 96 early (around step 42 of 89). DecoCal reached the correct answer, 104.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Fan Xu, Huixuan Zhang and Xiaojun Wan. 2026. DecoCal: Decoding with Calibration in Diffusion Large Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2026decocal,
  title = {{DecoCal: Decoding with Calibration in Diffusion Large Language Models}},
  author = {Fan Xu, Huixuan Zhang and Xiaojun Wan},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.545},
  url = {https://doi.org/10.18653/v1/2026.acl-long.545}
}
```

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*Updated 2 November 2026*