3 papers
cs.CL2026
A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answering
Gianluca Bonifazi, Christopher Buratti, Michele Marchetti +5
Retrieval-Augmented Generation (RAG) systems for multi-hop Question Answering (QA) must balance retrieval quality with computational cost. This cost is incurred during indexing tim…
cs.CL2026
Latent Trajectory Discrimination for AI-Generated Text Detection
Gianluca Bonifazi, Christopher Buratti, Michele Marchetti +5
Most existing approaches to AI-Generated Text Detection (AIGTD) treat documents as static objects and base their decisions on aggregate statistics or globally compressed embeddings…
cs.CL2026
Efficient Retrieval-Augmented Generation via Token Co-occurrence Graphs
Gianluca Bonifazi, Christopher Buratti, Michele Marchetti +5
Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by grounding the generation process on external knowledge. However, standard RAG appro…