arXiv:2602.05533v3 Announce Type: replace Abstract: We study conditional generation in diffusion models under hard constraints, where generated samples must satisfy prescribed events with probability one. Such constraints arise naturally in safety-critical applications and in rare-event simulation, where soft or reward-based guidance methods offer no guarantee of constraint satisfaction. Building on a probabilistic interpretation of diffusion models, we develop a principled conditional diffusion guidance framework based on Doob's h-transform, martingale representation and quadratic variation p
The increasing deployment of AI in safety-critical applications necessitates more robust and reliable methods for controlling generated outputs, making hard constraints a timely research focus.
This development addresses a critical limitation in current generative AI, enabling its use in domains where probabilistic constraint satisfaction is insufficient, thereby expanding the utility and trustworthiness of AI models.
AI systems can now be designed with guaranteed adherence to specific, non-negotiable conditions, potentially opening up new applications in highly regulated or sensitive environments.
- · AI safety researchers
- · Developers of autonomous systems
- · Regulators of AI
- · Industries with high safety standards (e.g., aerospace, healthcare)
- · Developers relying solely on soft guidance methods
- · Applications where safety is not rigorously defined
Diffusion models gain a crucial capability for hard constraint satisfaction, increasing their reliability in critical applications.
This improved reliability leads to broader adoption of generative AI in fields previously hesitant due to safety concerns.
The principle of hard constraint guidance could become a standard requirement for certifying AI systems in safety-critical deployments, shifting development paradigms.
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Read at arXiv cs.AI