The October 15, 2025 paper details a novel information retrieval framework called **LATTICE**, which uses a Large Language Model (LLM) to perform **hierarchical retrieval** over a large document corpus. This approach addresses the limitations of traditional retrieve-then-rerank and generative methods by organizing documents into a semantic tree structure offline, allowing the LLM to navigate the corpus with **logarithmic search complexity**. The core innovation lies in the online traversal stage, where a "search LLM" uses **calibrated latent relevance scores** to guide a greedy search across branches and levels of the tree, ensuring a globally coherent and efficient search. Experiments on the reasoning-intensive BRIGHT benchmark demonstrate that the **zero-shot LATTICE** framework achieves state-of-the-art recall and highly competitive ranking performance compared to specialized baselines, showing promise for more deeply integrated, LLM-native retrieval systems. Ablation studies confirm the critical roles of **score calibration** and **path relevance smoothing** in the algorithm's effectiveness.

Source:

https://arxiv.org/pdf/2510.13217