Lines of inquiry this post opens
Not questions with answers — ways of approaching this material across the collection.
- Does recursive generalization explain why neurons develop single interpretable functions?
- Do dense models already achieve high interpretability without sparse training?
- Why did neuron interpretability jump from near-zero to over ninety percent?
- How much do measurement thresholds affect estimates of neuron interpretability rates?
- Is the superposition hypothesis necessary to explain neural network behavior?
- Can mechanistic interpretability findings guide practical interventions in model design?
- How do probe-based interventions in activation space compare to mechanistic interpretability approaches?
- How do mechanistic interpretability and scientific understanding relate to each other?
- How should disciplines evaluate theories built on opaque machine learning predictions?