arXiv:2605.06137v2 Announce Type: replace-cross Abstract: In this work, we propose Prologue, an approach to bridging the reconstruction-generation gap in autoregressive (AR) image generation. Instead of modifying visual tokens to satisfy both reconstruction and generation, Prologue generates a small set of prologue tokens prepended to the visual token sequence. These prologue tokens are trained exclusively with the AR cross-entropy (CE) loss, while visual tokens remain dedicated to reconstruction. This decoupled design lets us optimize generation through the AR model's true distribution withou
The paper addresses an ongoing challenge in autoregressive visual generation, reflecting continuous research efforts to improve AI model efficiency and output quality.
Improving autoregressive visual generation fidelity directly impacts advancements in synthetic media, virtual environments, and potentially human-computer interaction.
This research offers a method to decouple reconstruction and generation within autoregressive models, potentially leading to more efficient and higher-quality visual AI outputs.
- · AI researchers
- · Creative industries relying on AI art
- · Generative AI model developers
Improved visual quality and computational efficiency in autoregressive image generation models.
Faster development and deployment of advanced visual AI applications across various sectors.
Enhanced realism in synthetic data and virtual realities, blurring the lines between real and generated content.
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