3 papers
cs.IR2026
STORM: Stepwise Token Optimization with Reward-Guided Beam Search
Arthur Satouf, Giulio D'Erasmo, Yuxuan Zong +3
Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the entire corpus into a specialized index, rebuilt whenever…
cs.IR2026
Statistical Foundations of DIME: Risk Estimation for Practical Index Selection
Giulio D'Erasmo, Cesare Campagnano, Antonio Mallia +3
High-dimensional dense embeddings have become central to modern Information Retrieval, but many dimensions are noisy or redundant. Recently proposed DIME (Dimension IMportance Esti…
cs.IR2024
ECLIPSE: Contrastive Dimension Importance Estimation with Pseudo-Irrelevance Feedback for Dense Retrieval
Giulio D'Erasmo, Giovanni Trappolini, Nicola Tonellotto +1
Recent advances in Information Retrieval have leveraged high-dimensional embedding spaces to improve the retrieval of relevant documents. Moreover, the Manifold Clustering Hypothes…