Never Lose the Thread: Collaborative Latent Reasoning for Generative Recommendation

Generative Recommendation (GR) reformulates item recommendation as autoregressive generation over structured item identifiers. Recent studies enhance GR through collaborative alignment and latent reasoning, incorporating behavioral preference and intermediate computation into semantic ID generation. However, these methods overlook collaborative continuity throughout hierarchical generation, i.e., action supervision leaves collaborative preference underdetermined in intermediate states and autoregressive path scoring fails to fully retain collaborative preference for the resulting item ranking. To address the above issues, we first identify the collaborative continuity gap, measured by the divergence between level-wise collaborative preference and intermediate-state preference. Subsequently, we propose CoLaGR, a collaborative latent reasoning framework for generative recommendation. CoLaGR derives level-wise collaborative preference from item-level behavioral signals. Furthermore, we develop latent reasoning guided by collaborative preference, where each reasoning state is grounded by the corresponding level-wise collaborative preference. Finally, collaborative leaf calibration is introduced using our constructed collaborative memory to refine complete-item ranking after generation. Extensive experiments demonstrate that CoLaGR consistently outperforms SOTA baselines, with improved collaborative preference modeling at both intermediate-decision and complete-item levels.