LLMs Need Encoders for Semantic IDs Too
Multimodal LLMs use dedicated encoders to bridge non-language modalities (vision encoders for images, depth models for audio codec tokens) because raw token embeddings alone cannot capture modality-specific structure. We argue that Semantic IDs (SIDs), the hierarchical codes used in generative recommendation, constitute another such modality: a SID level token's meaning depends on its prefix context, yet current systems simply add SID tokens to the vocabulary and rely on training to learn these context-dependent meanings from scratch. We propose PrefixMem, a lightweight SID encoder based on prefix n-gram memory tables that provides the LLM with structured, prefix-conditioned representatio...
| Authors | Xiangyi Chen, Zelun Wang, Xinyi Li et al. |
|---|---|
| Primary category | cs.IR |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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