OxygenFlowRec: Semantic-ID Recommendation via One-Step Continuous Generation

Semantic-ID (SID) recommenders commonly predict discrete code tuples, coupling recommendation to token-level decoding and, in beam-based methods, a search budget. We ask whether SID-based semantic sharing and SID outputs can be retained without discrete code prediction. We propose OxygenFlowRec, a catalogue-grounded SID recommender based on one-step continuous generation. Fixed SID assignments define parameter sharing, while task-trained code embeddings and frequency-gated item residuals learn recommendation-specific geometry. Conditioned on user history, the model generates a continuous query in a single update, ranks catalogue items by similarity, and returns their SIDs through deterministic lookup while preserving exact item identities. Training combines target recovery with supervision of the actual inference endpoint. To accommodate the different gradient aggregation patterns of shared codes and item residuals, we assign separate learning-rate scales to these parameter groups. This design separates the continuous generation process from the discrete SID output interface, avoiding token-level decoding and beam search. Experiments on six Amazon Reviews 2023 categories demonstrate competitive recommendation accuracy, while inference benchmarks show substantially lower retrieval cost relative to our beam-based LLaDA-Rec reproduction.