LessWrong (30+ Karma) “An Ambitious Vision for Interpretability” by leogao
Dec 5, 2025
Leo Gao, a researcher in mechanistic interpretability and AI alignment, discusses his ambitious vision for understanding neural networks. He highlights the importance of mechanistic understanding, likening it to switching from print statement debugging to using an actual debugger for clearer diagnostics. Gao shares recent advances in circuit sparsity, making circuits simpler and more interpretable. He also outlines future research directions, emphasizing that ambitious interpretability, although challenging, is crucial for safer AI development.
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Understanding Beats Surface Tests
- Mechanistic understanding reveals internal model behavior that external tests can miss, like scheming, making debugging far more reliable.
- Leo Gao compares this shift to moving from print statements to using an actual debugger for deep clarity.
Understand Why Solutions Work
- Understanding why an alignment approach works increases its robustness to future, different AGI architectures.
- Leo Gao warns that fixes without deep understanding are brittle and can fail unexpectedly.
Good Feedback Loops Drive Progress
- AMI benefits from measurable feedback loops and progressively stronger interpretability metrics.
- Leo Gao argues pushing frontier metrics drives real progress even without a watertight definition of understanding.
