Retrieval Grounding Latent Reasoning for Dense Retrieval

• URL: http://arxiv.org/abs/2608.14107v1
• Date: Fri, 14 Aug 2026 09:08:03 GMT
• Title: Retrieval Grounding Latent Reasoning for Dense Retrieval
• Abstract summary: Reasoning-intensive retrieval requires reasoning needed to determine relevance under a given instruction.<n>We propose Retrieval Grounding Latent Reasoning (RGLT), a latent reasoning framework for dense retrieval.<n> Experiments on reasoning-intensive retrieval benchmarks show that RGLT consistently outperforms strong baselines.
• Score: 30.66182684102075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 connects intermediate latent transitions with retrieval improvements. RGLT performs non-autoregressive reasoning in hidden space through an instruction-conditioned latent reasoning trajectory constructed from silent tokens. It combines process-supervised explicit-to-implicit distillation with retrieval-grounded supervision, using stage-wise CoT reconstruction to shape intermediate latent states and retrieval-effect credit to optimize incremental retrieval gains across the latent reasoning trajectories. Experiments on reasoning-intensive retrieval benchmarks show that RGLT consistently outperforms strong baselines while preserving efficient embedding inference.

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