Interference Beyond Geometry in Concept Extraction

Abstract:Interference is commonly treated as geometric overlap between learned features. We introduce effective interference, which combines feature geometry and code statistics to capture realized interactions, distinguishing constructive from destructive interference and frequent weak interactions from rare strong ones. Under local fixed-support assumptions, we characterize how architectural constraints shape interference through four mechanisms: feature orthogonalization, bias compensation, gain adaptation, and encoder-decoder separation. Experiments with sparse autoencoders show that constrained architectures selectively reduce overlap among co-active features, while bias, gain, and encoder freedom allow constructive cross-contributions to remain. Together, these results show that interference in learned representations depends not only on feature geometry, but also on how features are used and on the architecture that produces their codes.

Machine Learning (cs.LG)

arXiv:2609.35351 [cs.LG]

(or arXiv:2609.35351v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.35351 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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