The Emergence and Sparsification of a Syntactic Circuit

Mechanistic interpretability usually studies fully trained models, yet the computations that drive a behaviour can change while the model is still learning the task. On the Indirect Object Identification task, a model should continue with the name mentioned once rather than the name mentioned twice. Pythia models pass through an early training window in which they prefer the repeated name, so accuracy in a choice between the two names falls below one half while language-model loss on a fixed text sample keeps decreasing across the same window. The window reflects a temporary imbalance between two computations. We identify one cause of the wrong preference by selecting a set of attention heads that write the repeated name, on prompts separate from those used for causal evaluation, keeping that selection fixed, and then replacing each head's final-token output with its average output on a separate set of non-repeated-name prompts. This improves the correct-minus-repeated logit difference in a separately trained 160M model and in the official 160M, 410M, and 1B models. At 160M, the head that lowers the repeated name in the mature model shows little of its mature behaviour at this point. It directs less than one percent of its attention to the repeated mention, and its output makes almost no direct contribution to lowering that name's logit. Both properties grow over the interval in which behaviour recovers. Across the 160M, 410M, and 1B models, transplanting the corresponding head's mature parameters into the early checkpoint recovers 35 to 68 percent of the total improvement in the correct-minus-repeated logit difference seen by the end of training. Related early-to-late reversals appear at further Pythia scales, in two independently trained GPT-2 models, and in OLMo. A mature circuit can therefore conceal a transient causal configuration that shaped behaviour earlier in training.

Multilingual large language models often generalize across languages, and prior work suggests that their internal mechanisms can overlap cross-lingually. It remains unclear, however, when such sharing emerges and whether it varies with the overt realization of the same grammatical operation. We investigate this question for present-tense subject-verb agreement, a morphosyntactic process that varies substantially across languages and is only weakly expressed in English. Using activation patching and attention analysis across 29 languages and five open-source model families, we identify the attention heads causally implicated in agreement and compare these head-level signatures across languages. We find that languages with overt person/number inflection exhibit more similar agreement circuitry than non-conjugating languages, with the strongest sharing appearing when the analysis isolates recovery of the inflectional contrast itself. English provides an informative bridge case, becoming more similar to conjugating languages precisely in contexts where overt agreement is required. Finally, many implicated heads display similar attention patterns across languages, suggesting that cross-lingual overlap reflects shared functional roles as well as shared localization. Together, these results indicate that multilingual LLMs reuse partially shared computational structure for morphosyntactic agreement rather than relying on fully separate language-specific solutions.

A capability appears in a language model when the last parts of its circuit align in one stochastic attempt, and getting all but one right is worth nothing. We show this no-partial-credit joint alignment is the rate-limiting step of capability formation. Two fingerprints: in a shortcut-free apparatus a five-part circuit missing three waits as long as a three-part circuit missing three (1.19-1.37), so the wait counts missing parts, not size; and on Pythia across seven capabilities and three scales, ablating one part leaves a median 17% of the capability in 32 of 32 discriminating cells, where partial credit predicts 50-83% (p = 2e-10), while a random non-part head leaves 100%. One rare event whose barrier grows with missing parts yields a rate equation -- sites x attempts x drive x exp(-beta*K), minus destruction -- read three ways, each preregistered with frozen constants. Forward: a capability flat at baseline ignites at a step of our choosing once the mix passes a concentration floor (10/10 above, 0/12 below), and while still flat its arrival is datable from its precursor to 5% median error on six held-out models. Backward: the delay to learn a withheld capability grows with waiting until, past a critical step, it never ignites -- yet validation loss falls smoothly throughout, so standard monitors are blind to it. We locate the damage (heads commit to the base data) and isolate the cure: re-initializing only the query-key slices restores learnability (6/6) while the value slices do nothing (0/6). We prove the mechanism in a controlled gated-attention model: occupation forces a deadline whose consequences need no mixing assumption. Completed: SGD's noise fails the fluctuation-dissipation test, so we install one and anneal, melt and pin circuits on schedule. Scope: conjunction circuits in transformers to 1.4B.