Rethinking What LLMs Transfer to Recommendation: Localizing and Recombining Pretrained Computation

Large language model (LLM) initialization improves recommendation, with its benefits often attributed to general capabilities that existing methods seek to preserve. However, recovering general-task performance through mixed supervision brings no clear additional recommendation gain. Yet shared-neuron ablations reveal continued dependence on resources used by general tasks. What, then, does pretrained initialization supply that recommendation actually uses? We derive a cross-entropy training-gain bound in a simplified feed-forward model, separating the improvement available from initial computation from the cost of learning its recombination. Neuron-level analyses support this account: many channels used by the trained recommender are identifiable before training and remain useful as adaptation changes their task allocation. These findings motivate initial localization-guided neuron recombination, which selects channels from their initial activity on recommendation contexts and learns a low-rank residual mapping to recombine their responses after recommendation fine-tuning. Across three domains and four LLM backbones, our method consistently improves over standard fine-tuning, and ablations supporting both initial localization and learned recombination.