arXiv:2606.15819v1 Announce Type: cross Abstract: The rapid progress of visual autoregressive (VAR) models has unlocked a transformative frontier for high-fidelity text-to-image synthesis, while heightening concerns over the safety alignment of generated content. Naive application of existing erasure techniques to VAR models causes catastrophic semantic collapse and visual artifacts, since they are predominantly designed for the homogeneous denoising steps of diffusion models. To address this foundational challenge, we first propose the Semantic Singularity Axiom, which posits that any target
The rapid advancement of visual autoregressive models necessitates new techniques for steering and controlling their outputs to address safety and alignment concerns.
This research addresses a foundational challenge in controlling powerful generative AI, impacting the reliability and ethical deployment of text-to-image synthesis.
Current methods for controlling generative models are insufficient for visual autoregressive models, requiring novel approaches to prevent catastrophic semantic collapse.
- · AI safety researchers
- · Developers of text-to-image models
- · Platforms deploying generative AI
- · Malicious actors
- · Generative AI models without robust safety controls
Improved safety alignment and control in high-fidelity text-to-image synthesis.
Reduced incidence of harmful or unintended content generated by advanced visual AI.
Increased public and regulatory trust in the responsible development and deployment of generative AI technologies.
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Read at arXiv cs.AI