Shortcut or Semantics? Understanding Conditioning Interfaces for Steerable Generative Retrieval

Natural-language steering enables recommender systems to adapt to explicit user requests that go beyond historical interaction data. In generative retrieval, this requires conditioning a semantic ID (SID) decoder on textual preferences, but how best to interface language representations with the SID decoder remains unresolved. To address this, we propose a decoupled dual-branch interface that injects a frozen language model's final-layer representations via pooled global modulation and token-level cross-attention. We evaluate this design against end-to-end joint conditioning across three e-commerce domains. Joint conditioning reaches the highest retrieval accuracy, but suffers from catastrophic forgetting of general reasoning; meanwhile, the dual-branch interface trails by only 0.34 to 0.79 percentage points while preserving pretrained capabilities. Across meaning-preserving rephrasings, joint conditioning degrades sharply under synonym substitution, revealing that the model memorizes literal word matches instead of learning actual semantic intent. The dual-branch interface, in contrast, remains anchored in the frozen model's semantic space, exhibiting 55-74% lower degradation under lexical substitution across the three domains. These results establish our proposed dual-branch interface as a robust, semantically grounded foundation for steerable generative retrieval.