Hierarchical Latent Reasoning for LLM-based Recommendation
Large Language Models (LLMs) have shown strong potential for recommendation by leveraging their semantic understanding and contextual modeling capabilities. Recent studies further introduce reasoning mechanisms to improve user preference modeling. However, explicit natural-language reasoning incurs substantial inference overhead, whereas existing latent reasoning methods mainly focus on generating or verifying intermediate states, leaving their layer-wise preference roles and contributions insufficiently characterized. We propose HiLaR, a Hierarchical Latent Reasoning framework with layer-aware reinforcement optimization for LLM-based recommendation. HiLaR constructs temporal-guided hiera...
| Authors | Peiyu Hu, Siying Gu, Weihai Lu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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