Self-Reflective VLM with Multimodal Retrieval-Augmented Generation for Long-Context Construction Regulatory Document Comprehension
A deep comprehension of construction regulatory documents is essential for successful construction engineering and management. Although vision–language models (VLMs) hold tremendous potential for achieving human-level understanding, their practical deployment remains constrained by input size limitations. Even when integrated with retrieval-augmented generation (RAG), most efforts process visual elements commonly found in regulatory documents as pure texts and struggle to verify the trustworthiness of the retrieved supporting evidences, increasing vulnerability to hallucination. To address these drawbacks, we present a self-reflective multimodal RAG-assisted VLM pipeline that extends standard VLMs from single-pass document interpretation to multiround retrieval-grounded reasoning. Specifically, it augments existing VLMs with an external multimodal RAG mechanism, moving beyond text-only retrieval by jointly leveraging visual representations and semantic summaries to identify relevant evidence pages. Furthermore, an iterative self-reflection is incorporated to evaluate evidence sufficiency and trigger adaptive question refinement, thus enhancing response reliability. This pipeline was evaluated using a 794 question-answering pairs compiled from 12 divisions of a construction specification, covering three representative question types—information extraction, single-modal reasoning, and multimodal reasoning. Results showed substantial performance gains over traditional, single-model, and multimodal RAG-assisted baselines. More importantly, fine-grained analyses revealed that the proposed pipeline is particularly effective for complex engineering management scenarios—such as interpreting multimodal regulatory requirements, verifying compliance conditions embedded across late-document pages, and synthesizing information from text, tables, and figures. By enabling accurate, context-aware comprehension of lengthy construction regulatory documents without additional model training, the proposed pipeline offers a practical and generalizable tool for engineering management workflows. It has the potential to support compliance verification, reduce interpretation errors, and enhance the timeliness and quality of decision-making in construction management.