RePAIR: Improving RAG Without Error Categorization
A new arXiv preprint (2605.18772) introduces RePAIR, a response-action learning paradigm for Retrieval-Augmented Generation (RAG) that improves performance without relying on explicit error taxonomies or critic supervision. The method directly maps flawed RAG outputs to error-mitigating action plans, addressing the overlooked robustness of the error-correction process in agentic RAG systems. Across multiple benchmarks, RePAIR consistently enhances agentic RAG performance.
Key facts
- arXiv:2605.18772
- RePAIR is a response-action learning paradigm
- Improves RAG without explicit error categorization
- Maps flawed outputs to action plans
- Tested on multiple benchmarks
- Consistently improves agentic RAG performance
Entities
Institutions
- arXiv