Advanced AI systems may become dangerous not only because they become more capable, but because humans lose visibility into why models make decisions, when they are uncertain, and when they are behaving deceptively.
I believe one major bottleneck in AI safety is the lack of scalable interpretability infrastructure available outside frontier labs. Today, only a small number of organizations can deeply inspect model internals, limiting the amount of safety research the broader ecosystem can perform.
Stellaris-ModelScope reduces this bottleneck by democratizing mechanistic interpretability tooling.
If more researchers can inspect latent representations, activation pathways, confidence signals, and internal model geometry, we increase our ability to:
- detect hallucination before deployment
- identify deceptive or misaligned reasoning
- measure uncertainty more reliably
- develop intervention methods for safer model behavior
My research suggests that important safety-relevant signals, such as epistemic support, may already be linearly encoded inside transformer residual streams. If these internal signals can be reliably measured and acted upon, they could become a scalable oversight mechanism for advanced AI systems.