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DCGC is a Masked Diffusion Model framework that performs global correction of flawed reasoning traces in Large Language Models. It uses an imperfect solution draft from an upstream solver as auxiliary context and combines task‑specific supervised fine‑tuning with a Dynamic Dual‑CFG inference mechanism that separates problem‑only and joint problem‑draft branches. Experiments on math, code, and knowledge reasoning benchmarks show that DCGC outperforms standard sampling and simpler CFG variants, and it can improve full test‑set accuracy even when ground‑truth failure labels are unavailable.
Draft Refinement Language Models (DRLMs) propose parallel revisions to a draft answer using a single denoiser that reconstructs every token from uniformly corrupted drafts, enabling both preservation and repair of tokens. The model evaluates each candidate draft by estimating a cavity energy from forward predictions, selecting revisions that lower this energy without a separate scoring model. Experiments on Countdown, Sudoku-Extreme, and Maze-Hard show DRLM outperforming autoregressive and discrete diffusion baselines, achieving 44.4% on Countdown-5 and 93.9% on Sudoku-Extreme with 6M parameters.
Flow Language Models (FLMs) have emerged as a continuous-state alternative to discrete diffusion language models, yet the role of their continuous representations in reasoning remains unclear. We inve...
The paper introduces Stitching Noisy Diffusion Thoughts, a self‑consistency framework that transforms low‑cost diffusion‑sampled reasoning into a reusable pool of step‑level candidates. By sampling many diverse trajectories with a masked diffusion language model, scoring each intermediate step with a process reward model, and stitching the highest‑quality steps across trajectories, the method recomputes only the final answer, separating exploration from evaluation. Experiments on math reasoning benchmarks show that step‑level recombination improves accuracy by up to 23.8% on six tasks while reducing latency by up to 1.8× compared to traditional diffusion models and unified architectures.
Diffusion Language Models (DLMs) have recently become increasingly competitive with autoregressive (AR) models, and even outperform them on certain tasks. Unlike AR models, DLMs produce output through...
arXiv:2607. 18100v1 Announce Type: new Abstract: Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable.