UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation

• URL: http://arxiv.org/abs/2601.11522v1
• Date: Fri, 16 Jan 2026 18:59:58 GMT
• Title: UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation
• Abstract summary: We present UniX, a next-generation unified medical foundation model for chest X-ray understanding and generation.<n>UniX decouples the two tasks into an autoregressive branch for understanding and a diffusion branch for high-fidelity generation.<n>On two representative benchmarks, UniX achieves a 46.1% improvement in understanding performance and a 24.2% gain in generation quality.
• Score: 98.93314262366681
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite recent progress, medical foundation models still struggle to unify visual understanding and generation, as these tasks have inherently conflicting goals: semantic abstraction versus pixel-level reconstruction. Existing approaches, typically based on parameter-shared autoregressive architectures, frequently lead to compromised performance in one or both tasks. To address this, we present UniX, a next-generation unified medical foundation model for chest X-ray understanding and generation. UniX decouples the two tasks into an autoregressive branch for understanding and a diffusion branch for high-fidelity generation. Crucially, a cross-modal self-attention mechanism is introduced to dynamically guide the generation process with understanding features. Coupled with a rigorous data cleaning pipeline and a multi-stage training strategy, this architecture enables synergistic collaboration between tasks while leveraging the strengths of diffusion models for superior generation. On two representative benchmarks, UniX achieves a 46.1% improvement in understanding performance (Micro-F1) and a 24.2% gain in generation quality (FD-RadDino), using only a quarter of the parameters of LLM-CXR. By achieving performance on par with task-specific models, our work establishes a scalable paradigm for synergistic medical image understanding and generation. Codes and models are available at https://github.com/ZrH42/UniX.

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