🤖 AI Summary
This work addresses the limitations of traditional retrieval-augmented generation (RAG) in handling complex queries—such as multi-hop reasoning and structured knowledge acquisition—where static, single-step retrieval proves inadequate. The authors propose an agent-driven adaptive RAG framework that dynamically decomposes queries, performs iterative retrieval, and incorporates a lightweight self-reflection evaluation loop to adjust retrieval strategies on demand. For the first time, the study systematically compares the efficacy of query decomposition and reflection mechanisms in structured and multi-hop settings, revealing that agent augmentation is not universally beneficial and advocating for cost-sensitive, adaptive orchestration. Experiments show a 0.04 improvement in overall score and a 0.17 gain in MRR on the DevOps dataset; however, query decomposition reduces ranking accuracy in multi-hop tasks, and while reflection enhances citation precision, it introduces notable latency.
📝 Abstract
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding their responses in external knowledge, but conventional pipelines rely on static, single-step retrieval that limits performance on complex queries. This paper presents an Agent-Orchestrated Adaptive RAG framework that introduces dynamic query decomposition, iterative retrieval, and a bounded self-reflective evaluation loop. We evaluate the system across two complementary datasets: a domain-specific DevOps knowledge base and the multi-hop reasoning benchmark MuSiQue. Using metrics that include overall score, citation accuracy, mean reciprocal rank, and topic coverage, we find that query decomposition yields consistent gains in the structured domain (overall score $+0.04$, MRR $+0.17$ on DevOps) but degrades ranking precision on the multi-hop benchmark, while the reflection mechanism improves citation accuracy at a substantial latency cost. These contrasting results show that agentic enhancements are not universally beneficial and must be applied selectively according to query and domain characteristics. Our findings argue for adaptive, cost-aware orchestration rather than uniformly aggressive reasoning pipelines.