From Token Generation to Item Ranking: Direct Generative Recommendation with Semantic IDs

Generative recommendation formulates item recommendation as a token-level generation task, where Semantic IDs (SIDs) represents each item as a sequence of discrete tokens. However, recommendation ultimately requires item-level rankings, whereas SID-based methods derive them by decoding token-level outputs. We term these outputs the token interface; together, the interface and decoder form a token-mediated pipeline. We establish a theoretical dichotomy: if the interface is ranking-insufficient, no decoder based solely on it can guarantee exact ranking recovery; if it is ranking-sufficient, exact decoding is output-equivalent to item-level scoring. Thus, for item ranking, token-level generation either loses essential ranking information or provides no additional ranking expressiveness beyond direct item-level scoring. Based on this insight, we propose \textbf{Di}rect \textbf{G}enerative \textbf{R}ecommendation (\model), a framework that directly models item-level preferences while preserving the semantic structure of SID. Instead of treating SID tokens as generation targets, \model uses them as item representations and learns user-item matching through a unified item-level scoring function. Extensive experiments on multiple real-world datasets with LLM backbones of different scales demonstrate that \model consistently outperforms existing generative recommenders as well as ID-based methods.