01489nas a2200133   4500000000100000008004100001100001700042700001600059700001600075700001700091700002600108245013000134520109100264       2026                            d1 aZhenzhao Xia1 aBotao Zhong1 aShuai Zhang1 aTonghui Zhao1 aMirosław Skibniewski00aGraph-driven embedding reinforcement and traceable LLM agent for reliable element alignment in construction report generation3 a<p>Engineering report generation from construction-site Internet of Things (IoT) data using large language models (LLMs) remains challenging due to hallucinations. Ensuring traceability and reliability in information retrieval and multi-step reasoning is essential within retrieval-augmented generation (RAG) for LLM. This paper formalizes the RAG-LLM pipeline and proposes a dual-stream enhancement combining knowledge graph (KG) construction with reinforcement learning (RL)-based retriever tuning. The graph-guided module extracts structured engineering elements, while RL improves semantic alignment and tokenization of critical terms. Leveraging this dual-stream RAG, a traceable reporting agent is developed, providing end-to-end traceability of retrieval and reasoning, along with inter-step similarity measures. When collaborating with existing on-site IoT systems, the agent can extend automated monitoring to decision-making support. This paper presents a reliable approach for construction report generation and advances human-AI collaboration in construction management.</p>