Revisiting Modality Invariance in a Multilingual Speech-Text Model via Neuron-Level Analysis

Abstract: Multilingual speech-text foundation models aim to process language uniformly across both modality and language, yet it remains unclear whether they internally represent the same language consistently when it is spoken versus written. We investigate this question in SeamlessM4T v2 through three complementary analyses that probe where language and modality information is encoded, how selective neurons causally influence decoding, and how concentrated this influence is across the network. We identify language- and modality-selective neurons using average-precision ranking, investigate their functional role via median-replacement interventions at inference time, and analyze activation-magnitude inequality across languages and modalities. Across experiments, we find evidence of incomplete modality invariance. Although encoder representations become increasingly language-agnostic, this compression makes it more difficult for the shared decoder to recover the language of origin when constructing modality-agnostic representations, particularly when adapting from speech to text. We further observe sharply localized modality-selective structure in cross-attention key and value projections. Finally, speech-conditioned decoding and non-dominant scripts exhibit higher activation concentration, indicating heavier reliance on a small subset of neurons, which may underlie increased brittleness across modalities and languages.