Dopamine: Personalization as Brain Modes, Not Brains¹

Language-model personalization is usually represented in weight space: each user receives a separate parameter update, commonly a low-rank adapter. We ask whether this places personalization at the wrong representational level. Rather than treating each user as a different model, we propose that a single frozen model can express many personalized behavioral modes, selected by small user-specific states in activation space. We instantiate this view with Dopamine, which learns one additive residual vector per transformer layer while leaving all backbone parameters unchanged. Across three model families spanning 0.36B to 14B parameters, synthetic preference tasks, and real-user data, Dopamine matches the personalization quality of LoRA while requiring 100–215× less per-user state. Unlike separately instantiated adapters, activation modes can also be selected row by row, allowing users with different preferences to share a single forward pass without cross-user interference. Mechanistic ablations show that successful modes do more than perturb confidence: additive steering changes the model’s greedy token at roughly 55% of generated positions, whereas multiplicative modulation usually does not. Finally, although learned modes occupy a statistically low-dimensional space, aggressive shared-basis compression fails to preserve personalization quality, suggesting that behaviorally important user variation is not captured by variance alone. Together, these results support a different systems and modeling abstraction for personalization: not many personalized models, but many compact modes of one shared model.