3 papers
cs.IR2026
Utilizing Metadata for Better Retrieval-Augmented Generation
Raquib Bin Yousuf, Shengzhe Xu, Mandar Sharma +3
Retrieval-Augmented Generation systems depend on retrieving semantically relevant document chunks to support accurate, grounded outputs from large language models. In structured an…
cs.CL2025
Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects
Chris Latimer, Nicoló Boschi, Andrew Neeser +4
Agent memory has been touted as a dimension of growth for LLM-based applications, enabling agents that can accumulate experience, adapt across sessions, and move beyond single-shot…
cs.IR2025
QuOTE: Question-Oriented Text Embeddings
Andrew Neeser, Kaylen Latimer, Aadyant Khatri +2
We present QuOTE (Question-Oriented Text Embeddings), a novel enhancement to retrieval-augmented generation (RAG) systems, aimed at improving document representation for accurate a…