⚖️AI Alignment Forum•Stalecollected in 2m
Mechanistic Estimation Beats Sampling for Wide MLPs
💡Exact MLP output estimates without sampling—faster interpretability for alignment researchers.
⚡ 30-Second TL;DR
What Changed
Mechanistic estimate for random MLP outputs without model execution
Why It Matters
Advances alignment research by enabling precise, efficient analysis at initialization, potentially extensible to trained models for better interpretability.
What To Do Next
Clone the mlp_cumulant_propagation repo and test estimation on your wide random MLPs.
Who should care:Researchers & Academics
Key Points
- •Mechanistic estimate for random MLP outputs without model execution
- •Higher accuracy than sampling for wide models, proven theoretically
- •Open-source code in mlp_cumulant_propagation GitHub repo
- •Base case for broader mechanistic interpretability goals
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Original source: AI Alignment Forum ↗