SOURCE-LINKED INTELLIGENCE
Safin-1: Safety from Within through Memory-Native State Evolution
Long-horizon complex tasks require foundation models to accumulate information, maintain internal states, and adapt over extended interactions. Safety should be an intrinsic property of the model itself, rather than a behavioral constraint relying solely on external safeguards or post-hoc alignment such as supervised fine-tuning. This motivates Safety from Within, where safety-relevant capabilities are represented and invoked through the model's native computation. We present Safin-1, a family of foundation models realizing this principle through memory routing and state evolution. Safin-1 is
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T13:48:48.000Z
First collected: 2026-09-21T06:41:57.136Z. This is not the publication date.