Repetition Without Exclusivity: Scale Sensitivity of Referential Mechanisms in Child-Scale Language Models

We present the first systematic evaluation of mutual exclusivity (ME), the bias to map novel words to novel referents, in text-only language models trained on child-directed speech.We operationalise ME as referential suppression and train 45 GPT-2 models (2.9M-33.5Mparameters; 5-20 epochs on AO-CHILDES; 5 seeds) on a pre-registered ME battery.Across all 9 cells of the grid, models show robust anti-ME repetition priming, the opposite of ME, in 85-100% of items (all p < 2.5 × 10 -13 ), attenuating with improved language modelling (Spearman ρ = -0.533)but never crossing zero.A positive control on pretrained GPT-2 (124M-1.5B)shows the same priming, attenuating with scale but never reversing into significant ME, confirming the battery can detect suppression when present.The dissociation also holds on a size-matched non-CDS corpus (WikiText).A context-dependence diagnostic further shows that apparent ME-like patterns with nonce tokens reflect embedding similarity, not referential disambiguation.We connect this to the grounded cognition literature, where ME does emerge in visually grounded models, and argue that referential grounding, or another mechanism instantiating competition among candidate referents, may be required for ME-like behaviour to emerge.