RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval
  Augmented Generation Systems
Authors: name: Jingru Lin, name: Chen Zhang, name: Stephen Y. Liu, name: Haizhou Li
Organization: National University of Singapore

Retrieval-Augmented Generation (RAG) mitigates key limitations of Large
Language Models (LLMs)-such as factual errors, outdated knowledge, and
hallucinations-by dynamically retrieving external information. Recent work
extends this paradigm through agentic RAG systems, where LLMs act as agents to
iteratively plan, retrieve, and reason over complex queries. However, these
systems still struggle with challenging multi-hop questions, and their
intermediate reasoning capabilities remain underexplored. To address this, we
propose RAGCap-Bench, a capability-oriented benchmark for fine-grained
evaluation of intermediate tasks in agentic RAG workflows. We analyze outputs
from state-of-the-art systems to identify common tasks and the core
capabilities required for their execution, then construct a taxonomy of typical
LLM errors to design targeted evaluation questions. Experiments show that
"slow-thinking" models with stronger RAGCap performance achieve better
end-to-end results, underscoring the benchmark's validity and the importance of
enhancing these intermediate capabilities.