Generative retrieval offers a unified paradigm for personalized search by autoregressively decoding items as Semantic IDs. However, personalized search hinges on reasoning about query-history relevance, in which explicit textual reasoning incurs heavy cost and high latency, whereas purely implicit reasoning does not provide a structured intermediate latent interface for retrieval. To resolve this tension, we propose LaPR, an adaptive latent retrieval reasoning framework that turns explicit retrieval reasoning into compact latent signals. Specifically, LaPR constructs verified step-wise retrieval traces, induces step-conditioned semantic slots via vector quantization, and distills textual reasoning into these slots through a text-latent to pure-latent curriculum. At inference, LaPR adaptively predicts the reasoning depth and corresponding slots from the query and user history, providing latent reasoning signals for efficient Semantic-ID generation. Extensive experiments show that LaPR consistently surpasses all baselines, achieving SOTA personal retrieval performance while substantially reducing reasoning-token cost and inference latency.

In recent years, multilingual pre-trained language models (mPLMs) have achieved significant progress in cross-lingual dense retrieval. However, most mPLMs neglect the importance of knowledge. Knowledge always conveys similar semantic concepts in a language-agnostic manner, while query-passage pairs in cross-lingual retrieval also share common factual information. Motivated by this observation, we introduce KEPT, a novel mPLM that effectively leverages knowledge to learn language-agnostic semantic representations. To achieve this, we construct a multilingual knowledge base using hyperlinks and cross-language page alignment data annotated by Wiki. From this knowledge base, we mine intra- and cross-language pairs by extracting symmetrically linked segments and multilingual entity descriptions. Subsequently, we adopt contrastive learning with the mined pairs to pre-train KEPT. We evaluate KEPT on three widely-used benchmarks, considering both zero-shot cross-lingual transfer and supervised multilingual fine-tuning scenarios. Extensive experimental results demonstrate that KEPT achieves strong multilingual and cross-lingual retrieval performance with significant improvements over existing mPLMs.

Dense retrieval has achieved impressive advances in first-stage retrieval from a large-scale document collection, which is built on bi-encoder architecture to produce single vector representation of query and document. However, a document can usually answer multiple potential queries from different views. So the single vector representation of a document is hard to match with multi-view queries, and faces a semantic mismatch problem. This paper proposes a multi-view document representation learning framework, aiming to produce multi-view embeddings to represent documents and enforce them to align with different queries. First, we propose a simple yet effective method of generating multiple embeddings through viewers. Second, to prevent multi-view embeddings from collapsing to the same one, we further propose a global-local loss with annealed temperature to encourage the multiple viewers to better align with different potential queries. Experiments show our method outperforms recent works and achieves state-of-the-art results.