CircuitTree: Composing Circuits into A Syllable Transcriber

Abstract:The goal of circuit discovery is to decompose a monolithic neural network into tiny modular subnetworks which are interpretable in the sense that each does an identifiable subtask. We develop a practical method for composing arbitrarily many circuits into a single model. We propose a greedy, density-scaled composition algorithm that overcomes limitations of previous attempts to compose circuits by naive union, then generalize it to $N$ circuits via CircuitTree, a new data structure built on a $\Theta(N\log N)$ tree-compose schedule. We compose circuits pruned from HuBERT for classifying individual phonemes into a syllable classifier, a capability none of the 39 phoneme circuits has individually. Chaining CircuitTrees into a hierarchy-of-circuits pipeline, we transcribe open syllables at 64.8\% accuracy, within 1.2 points of a 66.0\% ceiling that shares no circuitry at all, with 99.5\% of circuit components shared.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Sound (cs.SD)

arXiv:2610.12520 [cs.LG]

(or arXiv:2610.12520v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.12520 Focus to learn more arXiv-issued DOI via DataCite

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