Closure-Validated Circuit Discovery in Attention Heads: Co-activation Proposes, Ablation Disposes
arXiv:2606.09607v1 Announce Type: new Abstract: Interpretability increasingly treats groups of components, not individual units, as the basic object, and proposes to find them by clustering co-activation statistics. We ask whether such a cheap signal actually identifies an attention-head circuit. Adapting a sparse-autoencoder clustering recipe to attention heads -- but validating by causal ablation rather than reconstruction -- we cluster heads and then run a closure test: ablate the discovered community and compare per-example damage to matched-random controls. Across two dense 1B-scale model
The increasing complexity of large language models necessitates better interpretability tools to understand their internal mechanisms and ensure reliability.
Understanding how AI models make decisions is crucial for developing safer, more controllable, and more efficient AI systems and for identifying and mitigating biases.
This research provides a more robust method for identifying and validating functional circuits within attention mechanisms, moving beyond statistical correlation to causal validation.
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Improved interpretability of AI models, particularly large language models.
Faster debugging and optimization of AI models, leading to more robust and performant systems.
Enhanced trust in AI systems and a reduction in AI safety incidents due to a deeper understanding of their failure modes.
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