Retrieval Grounding Latent Reasoning for Dense Retrieval
Reasoning-intensive retrieval requires text representations to capture not only semantic similarity, but also the reasoning needed to determine relevance under a given retrieval instruction. Existing reasoning-enhanced embedding models improve retrieval by incorporating reasoning information into dense representations, yet their supervision is typically dominated by the final retrieval objective. As a result, latent reasoning trajectories may learn shortcut reasoning patterns that preserve retrieval performance without producing meaningful incremental retrieval gains. We propose Retrieval Grounding Latent Reasoning (RGLT), a latent reasoning framework for dense retrieval that explicitly c...
| Authors | Gang Zhou, Xiongxi Yu, Hu Tian et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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