PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2601.11024v1
• Date: Fri, 16 Jan 2026 06:38:17 GMT
• Title: PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation
• Authors: Shuguang Jiao, Xinyu Xiao, Yunfan Wei, Shuhan Qi, Chengkai Huang, Quan Z. Michael Sheng, Lina Yao,
• Abstract summary: PruneRAG builds a structured query decomposition tree to perform stable and efficient reasoning.<n>We define the Evidence Forgetting Rate as a metric to quantify cases where golden evidence is retrieved but not correctly used.
• Score: 19.832367438725306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) has become a powerful framework for enhancing large language models in knowledge-intensive and reasoning tasks. However, as reasoning chains deepen or search trees expand, RAG systems often face two persistent failures: evidence forgetting, where retrieved knowledge is not effectively used, and inefficiency, caused by uncontrolled query expansions and redundant retrieval. These issues reveal a critical gap between retrieval and evidence utilization in current RAG architectures. We propose PruneRAG, a confidence-guided query decomposition framework that builds a structured query decomposition tree to perform stable and efficient reasoning. PruneRAG introduces three key mechanisms: adaptive node expansion that regulates tree width and depth, confidence-guided decisions that accept reliable answers and prune uncertain branches, and fine-grained retrieval that extracts entity-level anchors to improve retrieval precision. Together, these components preserve salient evidence throughout multi-hop reasoning while significantly reducing retrieval overhead. To better analyze evidence misuse, we define the Evidence Forgetting Rate as a metric to quantify cases where golden evidence is retrieved but not correctly used. Extensive experiments across various multi-hop QA benchmarks show that PruneRAG achieves superior accuracy and efficiency over state-of-the-art baselines.

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