Tingyu Song

Reasoning-intensive retrieval aims to surface evidence that maximizes downstream reasoning utility rather than only topical similarity. This capability is increasingly vital for agentic retriever-in-the-loop systems such as Deep-Research. However, existing retriever evaluation benchmarks, exempli...

Reasoning-Intensive Retrieval (RIR) targets retrieval settings where relevance is mediated by latent inferential links between a query and supporting evidence, rather than semantic similarity. Motivated by the emergent reasoning abilities of Large Language Models (LLMs), recent work integrates th...

Composed Image Retrieval (CIR) is a pivotal and complex task in multimodal understanding. Current CIR benchmarks typically feature limited query categories and fail to capture the diverse requirements of real-world scenarios. To bridge this evaluation gap, we leverage image editing to achieve pre...