What Multilingual Models Share: The Functional and Neural Significance of Representational Convergence

Multilingual models transfer knowledge across languages, offering a way to study shared principles of language organization. Interpreting representational convergence requires an account of how shared structure relates to computation and human processing. We compare 60 languages across nine models from five families and examine the functional and neural significance of shared organization using grammatical interventions and Chinese intracranial recordings. Relations among aligned meanings recur more strongly across model families than activation organization or sequential response strength, while local reconstruction depends on the source language. Edits to grammatical-number information selectively weaken English agreement for forms excluded from estimation in every model tested, and later edits offset the loss in most models; confined to the units that carry the recurrent relations, the edit retains 3–41% of its effect, within or below the range of matched random units, so grammatical influence is distributed beyond those units. In Qwen2.5-7B, English- and Hebrew-derived edits produce similar rankings of affected Hebrew sentences, while strength and surrounding number information shape effect magnitude. Chinese intracranial recordings from 21 participants distinguish overall neural resemblance from the additional correspondence to the same sentences, and the shared and model-specific parts of a representation differ in that correspondence by less than one tenth of the neural reliability reference. These findings clarify the functional and neural significance of shared linguistic knowledge, informing theories of multilingual intelligence that accommodate common organization and computational diversity. Code, results, and experimental settings are available at https://anonymous.4open.science/r/Multilingual-69FC/