arXiv:2606.05290v1 Announce Type: cross Abstract: Recent progress in generative modeling has made safety control a central challenge, yet existing approaches remain largely model-specific, requiring retraining or tailored interventions for each new architecture. In this work, we ask whether safety can be represented as a portable latent direction, learned once and reused across heterogeneous generators. We introduce the first framework for cross-model safety steering, in which a safety direction is estimated in a source LLM from paired safe-unsafe prompts, transported to a target generator thr
The proliferation of generative AI models necessitates a more efficient and universal approach to safety, moving beyond model-specific interventions.
This research proposes a method to generalize AI safety mechanisms across different models, potentially accelerating safe AI development and deployment.
Safety control for generative AI could become more portable and efficient, reducing the need for bespoke safety retraining for each new model.
- · AI developers
- · AI users
- · AI safety researchers
- · Generative AI platforms
- · Models requiring extensive, unique safety fine-tuning
This enables faster and wider adoption of new generative AI models due to inherent safety portability.
Standardized safety representations could foster collaboration and interoperability in AI development.
It might lower barriers to entry for new AI developers by providing foundational safety tools, potentially democratizing access to powerful AI.
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Read at arXiv cs.AI