ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding (Dec 2025)

Title: ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding (Dec 2025)
Link: http://arxiv.org/abs/2512.13586v1
Date: December 2025

Summary:
This paper introduces ReFusion, a novel masked diffusion model designed to overcome the efficiency and coherence limitations of existing parallel decoding methods. ReFusion employs a slot-based "plan-and-infill" strategy where a diffusion process first identifies weakly dependent slots, followed by parallel autoregressive decoding of those slots. A key architectural innovation allows ReFusion to achieve full Key-Value (KV) cache reuse—typically impossible in bidirectional diffusion models—by dynamically reordering generated slots. Extensive experiments show ReFusion outperforms prior masked diffusion models by 34% while being 18x faster, and surpasses strong autoregressive models like Qwen3-8B in performance with a 2.33x speedup.

Key Topics:
- Masked Diffusion Models
- Parallel Decoding
- Autoregressive Models
- KV Cache Reuse
- Plan-and-Infill
- Slot-based Generation
- Large Language Models

Chapters:
00:00 - Introduction to ReFusion
01:29 - Inference Latency Bottlenecks
03:07 - MDM Coherence Challenges
04:15 - The Slot-Based Architecture
05:35 - Achieving Full KV Reuse
07:26 - Step 1: Slot Planning
08:08 - Step 2: Verification and Infilling
09:58 - Benchmarking and Robustness
11:40 - Non-Linear Coding Example
12:45 - Limitations and Future Outlook

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