AI Research
Representational Simplicity and Circuit Size Dissociate in a Threshold-Dependent Way: A Controlled Test via Adversarial Training
Uncovering the complex relationship between model interpretability and actual computational overhead.
Sparse-autoencoder decomposability and concentrated feature attribution are increasingly treated as evidence that a model's computation is easier to reverse-engineer. Whether this representational and attributional cleanliness actually predicts a smaller or more tractable causal ...
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ArXiv CS.AI
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ArXiv CS.AI
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This research challenges the assumption that clean, interpretable representations correlate with simpler internal circuitry. It suggests that mechanistic interpretability may be harder to achieve than previously thought, as model complexity persists even when features appear sparse.
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