Abstract
Our present research aims to create a foundation for mechanistic interpretability research. In particular, we're focused on trying to resolve the challenge of superposition. In doing so, it's important to keep sight of what we're trying to lay the foundations for. This essay summarizes those motivating aspirations – the exciting directions we hope will be possible if we can overcome the present challenges.
We aim to offer insight into our vision for addressing mechanistic interpretability's other challenges, especially scalability. Because we have focused on foundational issues, our longer-term path to scaling interpretability and tackling other challenges has often been obscure. By articulating this vision, we hope to clarify how we might resolve limitations, like analyzing massive neural networks, that might naively seem intractable in a mechanistic approach.
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