RGLT: A Latent Reasoning Framework for Dense Retrieval
A recent paper published on arXiv (2608.14107) presents Retrieval Grounding Latent Reasoning (RGLT), a framework designed for dense retrieval through latent reasoning. This study tackles the problem where current reasoning-enhanced embedding models often adopt shortcut reasoning patterns, maintaining retrieval effectiveness without significant advancements. RGLT establishes a direct link between intermediate latent transitions and retrieval enhancements by utilizing non-autoregressive reasoning in hidden spaces, guided by an instruction-conditioned latent reasoning trajectory derived from silent tokens. By integrating process-supervised learning, the framework aims to align reasoning steps with retrieval results. Authored by a team of researchers, this work is pertinent to information retrieval and natural language processing, especially for reasoning-heavy retrieval tasks.
Key facts
- Paper arXiv:2608.14107 introduces RGLT, a latent reasoning framework for dense retrieval.
- RGLT addresses shortcut reasoning patterns in existing reasoning-enhanced embedding models.
- RGLT performs non-autoregressive reasoning in hidden space using silent tokens.
- The framework uses an instruction-conditioned latent reasoning trajectory.
- RGLT combines process-supervised learning to connect latent transitions with retrieval improvements.
- The paper was announced as a new type on arXiv.
- The research targets reasoning-intensive retrieval tasks.
- The framework aims to produce meaningful incremental retrieval gains.
Entities
Institutions
- arXiv