New benchmark assesses multimodal RAG systems

Researchers have developed FATHOMS-RAG, a new benchmark designed to evaluate the end-to-end performance of retrieval-augmented generation (RAG) systems. This framework assesses a RAG pipeline's ability to ingest, retrieve, and reason across various data modalities including text, tables, and images. The study found that closed-source RAG pipelines generally outperform open-source ones, particularly when dealing with complex multimodal and cross-document information. AI

Summary written by gemini-2.5-flash-lite from 1 sources. How we write summaries →

IMPACT Introduces a new evaluation framework for multimodal RAG systems, potentially driving improvements in their accuracy and reducing hallucinations.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating AI systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• FATHOMS-RAG
• Large Language Models
• Retrieval Augmented Generation

• paper
• product