RAGCap-Bench: A Fine-Grained Benchmark for Intermediate Capabilities in Agentic RAG
kalyan_kpl · x · 2026-11-10
A new AACL/IJCNLP 2026 paper introduces RAGCap-Bench, a capability-oriented benchmark for fine-grained evaluation of intermediate tasks in agentic RAG workflows.
- Motivation: Agentic RAG systems, where LLMs iteratively plan, retrieve, and reason, still struggle with challenging multi-hop questions, and their intermediate reasoning capabilities remain underexplored.
- Method: The authors analyzed outputs of state-of-the-art systems to identify common intermediate tasks and required capabilities, then built a taxonomy of typical LLM errors to design targeted evaluation questions.
- Findings: Experiments show that "slow-thinking" models with stronger RAGCap performance achieve better end-to-end results, validating the benchmark and highlighting the importance of improving these intermediate capabilities.
Authored by Jingru Lin, Chen Zhang, Yong Liu, and Haizhou Li; available in the ACL Anthology.
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