aiqbench analysis
This study demonstrates that clustering co-activation statistics yields valid circuit proposals in dense models only when validated via causal ablation, as statistical signals in Mixture-of-Experts architectures often fail this closure test. The findings underscore that cheap correlation metrics are insufficient for confirming functional circuits, necessitating rigorous ablation to distinguish genuine mechanisms from spurious patterns. For interpretability researchers, this establishes closure testing as the critical differentiator between a mere statistical cluster and a confirmed computational circuit.
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 models (Pythia 1B, OLMo 1B) and two input distributions, the communities pass closure. In a Mixture-of-Experts model (OLMoE-1B-7B), route-conditional clustering recovers a statistically real signal that nonetheless does not survive closure -- ablation improves loss, the wrong direction. Extending closure across training, attention-target selectivity and participation ratio decouple from function in both directions. We conclude that a cheap signal is a circuit proposal, not a confirmed circuit; closure is what separates them.
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 models (Pythia 1B, OLMo 1B) and two input distributions, the communities pass closure. In a Mixture-of-Experts model (OLMoE-1B-7B), route-conditional clustering recovers a statistically real signal that nonetheless does not survive closure -- ablation improves loss, the wrong direction. Extending closure across training, attention-target selectivity and participation ratio decouple from function in both directions. We conclude that a cheap signal is a circuit proposal, not a confirmed circuit; closure is what separates them.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.09607 [cs.LG]
(or arXiv:2606.09607v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.09607 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 245 words