@inproceedings{zeng-etal-2026-racc,
title = "{RACC}: Regret-Aware Confidence Calibration for Consistent Masked Discrete Diffusion Decoding",
author = "Zeng, Qinglin and
Zhang, Jusheng and
Yang, Jing and
Liu, Ningyuan and
Wang, Keze",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1138/",
doi = "10.18653/v1/2026.findings-acl.1138",
pages = "22656--22672",
ISBN = "979-8-89176-395-1",
abstract = "Masked Discrete Diffusion Models (MDMs) enable parallel generation via iterative refinement. However, we identify a critical decisional mismatch. The MDM architecture is inherently dynamic and capable of sensing context shifts. In contrast, prevailing decoding paradigms remain static and myopic. They treat each denoising step as an isolated snapshot, effectively discarding valuable temporal feedback that signals logical conflicts. To bridge this gap, we propose Regret-Aware Confidence Calibration (RACC). This training-free framework aligns decoding decisions with the model{'}s latent self-correction capabilities. RACC introduces a momentum anchor to track confidence trajectories. When a token{'}s probability drops abruptly below its historical trend, the system triggers a ``regret'' signal. Unlike expensive re-masking or lookahead search, RACC utilizes this signal to proactively demote unstable candidates. Extensive experiments on reasoning benchmarks, such as HumanEval and GSM8K, demonstrate that RACC significantly improves generation consistency. Crucially, RACC achieves these gains with zero additional inference overhead, effectively balancing decoding quality and efficiency."
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<abstract>Masked Discrete Diffusion Models (MDMs) enable parallel generation via iterative refinement. However, we identify a critical decisional mismatch. The MDM architecture is inherently dynamic and capable of sensing context shifts. In contrast, prevailing decoding paradigms remain static and myopic. They treat each denoising step as an isolated snapshot, effectively discarding valuable temporal feedback that signals logical conflicts. To bridge this gap, we propose Regret-Aware Confidence Calibration (RACC). This training-free framework aligns decoding decisions with the model’s latent self-correction capabilities. RACC introduces a momentum anchor to track confidence trajectories. When a token’s probability drops abruptly below its historical trend, the system triggers a “regret” signal. Unlike expensive re-masking or lookahead search, RACC utilizes this signal to proactively demote unstable candidates. Extensive experiments on reasoning benchmarks, such as HumanEval and GSM8K, demonstrate that RACC significantly improves generation consistency. Crucially, RACC achieves these gains with zero additional inference overhead, effectively balancing decoding quality and efficiency.</abstract>
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%0 Conference Proceedings
%T RACC: Regret-Aware Confidence Calibration for Consistent Masked Discrete Diffusion Decoding
%A Zeng, Qinglin
%A Zhang, Jusheng
%A Yang, Jing
%A Liu, Ningyuan
%A Wang, Keze
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zeng-etal-2026-racc
%X Masked Discrete Diffusion Models (MDMs) enable parallel generation via iterative refinement. However, we identify a critical decisional mismatch. The MDM architecture is inherently dynamic and capable of sensing context shifts. In contrast, prevailing decoding paradigms remain static and myopic. They treat each denoising step as an isolated snapshot, effectively discarding valuable temporal feedback that signals logical conflicts. To bridge this gap, we propose Regret-Aware Confidence Calibration (RACC). This training-free framework aligns decoding decisions with the model’s latent self-correction capabilities. RACC introduces a momentum anchor to track confidence trajectories. When a token’s probability drops abruptly below its historical trend, the system triggers a “regret” signal. Unlike expensive re-masking or lookahead search, RACC utilizes this signal to proactively demote unstable candidates. Extensive experiments on reasoning benchmarks, such as HumanEval and GSM8K, demonstrate that RACC significantly improves generation consistency. Crucially, RACC achieves these gains with zero additional inference overhead, effectively balancing decoding quality and efficiency.
%R 10.18653/v1/2026.findings-acl.1138
%U https://aclanthology.org/2026.findings-acl.1138/
%U https://doi.org/10.18653/v1/2026.findings-acl.1138
%P 22656-22672
Markdown (Informal)
[RACC: Regret-Aware Confidence Calibration for Consistent Masked Discrete Diffusion Decoding](https://aclanthology.org/2026.findings-acl.1138/) (Zeng et al., Findings 2026)
ACL