DCGC: Draft-Conditioned Global Correction for Complex Reasoning with Masked Diffusion Models

Abstract:Correcting flawed reasoning traces remains a significant challenge for Large Language Models (LLMs), whose autoregressive generation can propagate early mistakes into subsequent reasoning. We introduce DCGC, a Masked Diffusion Model (MDM) framework for global correction that uses an imperfect solution draft from an upstream solver as auxiliary context. DCGC combines task-specific Supervised Fine-Tuning (SFT) with a novel inference-time mechanism called Dynamic Dual-CFG. This mechanism separates problem-only and joint problem-draft branches and scales the draft-conditioned residual using a relative confidence gap. Across math, code, and knowledge reasoning benchmarks, DCGC outperforms standard sampling and simpler CFG variants, with additional results suggesting transfer to different diffusion backbones. In test-time setting where ground-truth failure labels are unavailable, DCGC improves full test set accuracy by correcting low-consensus upstream outputs, highlighting its utility as a verifier-free global correction module for difficult reasoning instances.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.25428 [cs.CL]

(or arXiv:2608.25428v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.25428 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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