Trust-RAG: Agentic Retrieval-Augmented Generation with Evidence Verification and Consistency-Aware Reasoning

Retrieval-augmented generation (RAG) improves language models by providing relevant external information during answer generation. However, retrieved ev- idence can be low-quality or contradictory. When unreliable evidence is passed directly to the language model, it can lead to incorrect reasoning and halluci- nated answers. Trust-RAG is proposed as an agentic RAG framework designed to improve evidence reliability and consistency in multi-hop question answering. Trust-RAG consists of three agents: Retrieval, Verification, and Consistency. This is coordinated by an LLM-based controller. The Retrieval Agent adaptively se- lects the query and retrieval depth, the Verification Agent evaluates and filters retrieved documents, and the Consistency Agent identifies contradiction-free ev- idence and determines whether additional retrieval is required. The controller coordinates these decisions and can initiate re-retrieval when the available evi- dence is insufficient. The framework combines BM25 and dense retrieval to form a hybrid retrieval system, verifies evidence using relevance and NLI signals, ap- plies consistency-graph reasoning, and uses Qwen2.5-7B-Instruct for decision- making and answer generation. The system evaluates Trust-RAG on HotPotQA and GSM8K. On the full HotpotQA distractor validation set (n = 7,405), Trust- RAG achieves an Exact Match of 0.423 and an F1 score of 0.535, with selected evidence covering the gold supporting documents in 58.7% of examples. On GSM8K, Trust-RAG achieves a numerical Exact Match of 0.830 on the full test set (n = 1,319).