COIN: Collaborative Intent for Efficient Reasoning in Native Generative Recommendation

Reasoning can improve generative recommendation (GenRec) by forming intermediate hypotheses that guide item prediction. While reasoning is natural for LLM-powered GenRec, introducing it into native recommenders is less straightforward due to the lack of a natural language interface. We introduce COIN (Collaborative Intent-aware Generative Recommendation), a lightweight plan-then-execute framework that grounds reasoning in a collaborative intent space. COIN represents an intent as a coarse collaborative region learned from interactions and expresses the plan through a single token, providing guidance complementary to item semantics. During training, a lightweight forecaster supplies plausible intents, and ambiguity-aware transfer trains the generator to predict the target item under planning uncertainty. COIN is trained solely through next-token prediction and does not rely on reinforcement learning, LLM supervision, or recurrent refinement. At inference, the generator predicts and executes its own plan with only one additional token. COIN accommodates different generator architectures and semantic-ID tokenizers, serving as a plug-in for existing native GenRec models. Experiments on four recommendation datasets demonstrate consistent ranking improvements with minimal training and inference overhead, establishing collaborative intent as an efficient interface for reasoning in native generative recommendation.