Xueguang Ma

Deep search agents that combine large language models with retrieval tools excel at complex, multi-hop queries. Yet, existing benchmarks such as BrowseComp rely on black-box web search APIs, facing key limitations. (1) Fairness: for agents, dynamic and opaque web APIs hinder reproducibility and f...

We introduce Rank-R1, an LLM-based reranker that reasons over queries and candidate documents before ranking. Existing rerankers based on LLMs rely on prompting or fine-tuning to order documents by relevance without explicit reasoning. Rank-R1 uses reinforcement learning with only relevance label...

While dense retrieval models have been the standard for state-of-the-art information retrieval, their deployment is often constrained by high memory requirements and reliance on GPU accelerators for vector similarity search at scale. Learned sparse retrieval offers a compelling alternative by ena...