RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems
arXiv:2510.13910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) mitigates key limitations of Large Language Models (LLMs)-such as factual errors, outdated knowledge, and hallucinations-by dynamically retrieving external information.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 ca
The proliferation of LLMs necessitates more robust evaluation methods for their advanced agentic behaviors, especially as they move toward complex problem-solving.
Improved benchmarking for agentic RAG systems is crucial for developing reliable and powerful AI agents capable of addressing complex, multi-step queries.
The proposed RAGCap-Bench provides a standardized tool to measure LLM capabilities in iterative planning, retrieval, and reasoning within agentic RAG, highlighting current limitations in multi-hop questions.
- · AI researchers
- · LLM developers
- · Enterprises deploying RAG systems
- · LLMs with poor agentic reasoning
- · Current RAG systems struggling with multi-hop queries
Researchers gain better tools to evaluate and improve agentic RAG systems.
This leads to the development of more capable and reliable AI agents for complex tasks.
Advanced agentic systems begin to automate more nuanced decision-making and research processes across industries.
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Read at arXiv cs.CL