Legal compliance in AI-driven data transfer planning is becoming increasingly critical under stringent privacy regulations such as the Japanese Act on the Protection of Personal Information (APPI). We propose a multi-agent legal verifier that decomposes compliance checking into specialized agents for statutory interpretation, business context evaluation, and risk assessment, coordinated through a structured synthesis protocol. Evaluated on a stratified dataset of 200 Amended APPI Article 16 cases with clearly defined ground truth labels and multiple performance metrics, the system achieves 72% accuracy, which is 21 percentage points higher than a single-agent baseline, including 90% accuracy on clear compliance cases (vs. 16% for the baseline) while maintaining perfect detection of clear violations. While challenges remain in ambiguous scenarios, these results show that domain specialization and coordinated reasoning can meaningfully improve legal AI performance, providing a scalable and regulation-aware framework for trustworthy and interpretable automated compliance verification.

Building officials, especially those in resource - constrained or rural jurisdictions, struggle with labor - intensive, error - prone, and costly manual reviews of design documents as projects scale in size and complexity. Widespread adoption of Building Information Modeling (BIM) and Large Language Models (LLMs) has created opportunities for automated code review (AC R) solutions . This study proposes a novel agent - driven framework that integrates BIM - based data extraction with automated verification using both re trieval - augmented generation (RAG) and Model Context Protocol (MCP) agent pipelines. The framework employs LLM - enabled agents to extract geometry, schedules, and system attributes from heterogeneous file types, which are then processed for building code checking via two complementary mechanisms: (i) direct API calls to DOE's COMcheck engine, providing deterministic and audit - ready outputs, and (ii) RAG - based reasoning over rule provisions, allowing flexible interpretation where coverage is incomplete or amb iguous . The framework was evaluated through case demonstrations, including automated extraction of geometric attributes (e.g., surface area, tilt, and insulation values), parsing of operational schedules, and design validation for lighting allowances under ASHRAE Standard 90.1 - 2022. Comparative performance tests across multiple large language models showed that Generative Pre - trained Transformer 4 Omni (GPT - 4o) achieved the best balance of efficiency and stability, while smaller models exhibited inconsistenc ies or failure s . Results confirm that MCP agent pipelines perform better than RAG reasoning pipelines on rigor and flexibility in workflows.

ABSTRACT Building compliance checking (BCC) is a critical process for ensuring that constructed facilities meet regulatory standards. A core component of BCC is the accurate enumeration of facility types and their spatial distribution. Despite its importance, this problem has been largely overlooked in the literature, posing a significant challenge for BCC and leaving a critical gap in existing workflows. Performing this task manually is time-consuming and labor-intensive. Recent advances in large language models (LLMs) offer new opportunities to enhance automation by combining visual recognition with reasoning capabilities. In this paper, we introduce a new task for BCC: automated facility enumeration, which involves validating the quantity of each facility type against statutory requirements. To address it, we propose a novel method that integrates door detection with LLM-based reasoning. We are the first to apply LLMs to this task and further enhance their performance through a Chain-of-Thought (CoT) pipeline. Experiments on both real-world and synthetic floor plan data demonstrate the effectiveness and robustness of our method. PRACTICAL APPLICATIONS This work demonstrates the potential of LLMs to achieve accurate and generalizable automated facility enumeration.

This study evaluates the implementation of a Generative AI-powered rule checking workflow within a graduate-level Building Information Modeling (BIM) course at a U.S. university. Over two semesters, 55 students participated in a classroom-based pilot exploring the use of GenAI for BIM compliance tasks, an area with limited prior research. The instructional design included lectures on prompt engineering and AI-driven rule checking, followed by an assignment where students used a large language model (LLM) to identify code violations in designs using Autodesk Revit. Surveys and interviews were conducted to assess student workload, learning effectiveness, and overall experience, using the NASA-TLX scale and regression analysis. Findings indicate students generally achieved learning objectives but faced challenges such as difficulties debugging AI-generated code and inconsistent tool performance, probably due to their limited prompt engineering experience. These issues increased cognitive and emotional strain, especially among students with minimal programming backgrounds. Despite these challenges, students expressed strong interest in future GenAI applications, particularly with clear instructional support.

This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling (BIM) by introducing a Large Language Model (LLM)-driven approach to semi-automate this critical process. The developed system integrates LLMs such as GPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks within the BIM environment. Case studies on a single-family residential project and an office building project demonstrated the system's ability to reduce the time and effort required for compliance checks while improving accuracy. It streamlined the identification of violations, such as non-compliant room dimensions, material usage, and object placements, by automatically assessing relationships and generating actionable reports. Compared to manual methods, the system eliminated repetitive tasks, simplified complex regulations, and ensured reliable adherence to standards. By offering a comprehensive, adaptable, and cost-effective solution, this proposed approach offers a promising advancement in BIM-based compliance checking, with potential applications across diverse regulatory documents in construction projects.

Compliance checking is the process of determining whether a regulated entity adheres to these regulations. Currently, compliance checking is predominantly manual, requiring significant time and highly skilled experts, while still being prone to errors caused by the human factor. Various approaches have been explored to automate compliance checking, however, representing regulations in OWL DL language which enables compliance checking through OWL reasoning has not been adopted. In this work, we propose an annotation schema and an algorithm that transforms text annotations into machine-interpretable OWL DL code. The proposed approach is validated through a proof-of-concept implementation applied to examples from the building construction domain.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. The Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. In the latest interview, we hear from Joseph Marvin Imperial, who is focussed on aligning generative AI with technical standards for regulatory and operational compliance. Standards are documents created by industry and/or academic experts that have been recognized to ensure the quality, accuracy, and interoperability of systems and processes (aka "the best way of doing things"). You'll see standards in almost all sectors and domains, including the sciences, healthcare, education, finance, journalism, law, and engineering.

Automated Rule Checking (ARC) plays a crucial role in advancing the construction industry by addressing the laborious, inconsistent, and error-prone nature of traditional model review conducted by industry professionals. Manual assessment against intricate sets of rules often leads to significant project delays and expenses. In response to these challenges, ARC offers a promising solution to improve efficiency and compliance in design within the construction sector. However, the main challenge of ARC lies in translating regulatory text into a format suitable for computer processing. Current methods for rule interpretation require extensive manual labor, thereby limiting their practicality. To address this issue, our study introduces a novel approach that decomposes ARC into two distinct tasks: rule information extraction and verification code generation. Leveraging generative pre-trained transformers, our method aims to streamline the interpretation of regulatory texts and simplify the process of generating model compliance checking code. Through empirical evaluation and case studies, we showcase the effectiveness and potential of our approach in automating code compliance checking, enhancing the efficiency and reliability of construction projects.

This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs), within the building industry. By leveraging these advanced AI tools, the study explores their application across key areas such as energy code compliance, building design optimization, and workforce training. The research highlights how LLMs can automate labor-intensive processes, significantly improving efficiency, accuracy, and safety in building practices. The paper also addresses the challenges associated with interpreting complex visual and textual data in architectural plans and regulatory codes, proposing innovative solutions to enhance AI-driven compliance checking and design processes. Additionally, the study considers the broader implications of AI integration, including the development of AI-powered tools for comprehensive code compliance across various regulatory domains and the potential for AI to revolutionize workforce training through realistic simulations. This paper provides a comprehensive analysis of the current capabilities of generative AI in the building industry while outlining future directions for research and development, aiming to pave the way for smarter, more sustainable, and responsive construction practices.

Ensuring content compliance with community guidelines is crucial for maintaining healthy online social environments. However, traditional human-based compliance checking struggles with scaling due to the increasing volume of user-generated content and a limited number of moderators. Recent advancements in Natural Language Understanding demonstrated by Large Language Models unlock new opportunities for automated content compliance verification. This work evaluates six AI-agents built on Open-LLMs for automated rule compliance checking in Decentralized Social Networks, a challenging environment due to heterogeneous community scopes and rules. Analyzing over 50,000 posts from hundreds of Mastodon servers, we find that AI-agents effectively detect non-compliant content, grasp linguistic subtleties, and adapt to diverse community contexts. Most agents also show high inter-rater reliability and consistency in score justification and suggestions for compliance. Human-based evaluation with domain experts confirmed the agents' reliability and usefulness, rendering them promising tools for semi-automated or human-in-the-loop content moderation systems.