arXiv:2605.28093v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has emerged as a promising paradigm for enhancing large language models (LLMs) on multi-hop question answering (QA), which requires reasoning over evidence from multiple documents. Current multi-hop RAG methods generally focus on either query-side task decomposition or corpus-side knowledge graph construction. Despite their progress, these methods still struggle to achieve satisfactory performance on complex multi-hop QA tasks. To this end, we propose ConRAG, a consensus-driven multi-view RAG framework tha
The increasing complexity of multi-hop question answering and the limitations of current RAG methods are driving innovations like ConRAG to enhance LLM performance.
Improved multi-hop QA capabilities will significantly advance the practical utility and reliability of LLMs, enabling them to tackle more intricate reasoning tasks.
Traditional RAG methods are being superseded by more sophisticated frameworks that leverage consensus-driven multi-view retrieval, leading to more accurate and robust LLM outputs.
- · LLM developers
- · AI-powered search engines
- · Knowledge management systems
- · Data analysis platforms
- · LLM models without advanced retrieval mechanisms
- · Basic RAG implementations
LLMs will become more effective at complex reasoning over diverse information sources.
This improved reasoning ability could lead to the automation of more sophisticated information synthesis tasks.
Enhanced LLM capabilities might accelerate the development and deployment of AI agents capable of complex decision-making and problem-solving.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL