This paper proposes a novel pipeline that leverages large language models (LLMs) to derive legal driving requirements for autonomous vehicles (AVs) by grounding LLM reasoning in a traffic scenario taxonomy. The method significantly improves law-scenario matching and the accuracy of derived mandatory and prohibitive requirements, providing a solid foundation for lawful AV development and deployment.

This paper introduces UAF (Unified Audio Front-end LLM), a novel large language model that unifies critical audio front-end tasks like voice activity detection, speaker recognition, and automatic speech recognition into a single end-to-end generative framework. UAF aims to overcome the limitations of traditional cascaded pipelines and enhance full-duplex speech interaction by jointly modeling semantic content and interaction-level control signals.

This paper empirically evaluates AI‑assisted requirements engineering against expert judgments, showing AI can reliably handle initial quality checks but still relies on human expertise for deeper analysis.

This paper provides a comprehensive literature review on the application of Large Language Models (LLMs) in business process modeling, detailing their current integration into text-to-model pipelines and identifying key challenges and future research directions for supporting complex organizational process modeling.

Current methods using LLMs to translate technical XAI outputs into natural language often lack guarantees of accuracy and completeness. This paper introduces a Two-Stage LLM Meta-Verification Framework that employs an Explainer LLM for generating explanations and a Verifier LLM to assess and refine them iteratively, significantly enhancing the trustworthiness and accessibility of XAI.

This paper proposes viewing large language model (LLM) teams through the lens of distributed systems to create a principled framework for their design and evaluation. It reveals that many established advantages and challenges from distributed computing, such as scalability limits and coordination issues, directly apply to and explain the behavior of LLM teams.

This paper introduces the Hierarchical Autonomy Evolution (HAE) framework, a novel approach to categorizing security vulnerabilities in AI agents as they evolve from cognitive entities to collective societies. It details a taxonomy of threats across three levels of autonomy, highlighting critical research gaps and guiding the development of robust, multilayered defense architectures for trustworthy AI agent systems.

The Auton Agentic AI Framework introduces a principled architecture to bridge the gap between stochastic Large Language Model outputs and the deterministic requirements of backend systems, standardizing the creation, execution, and governance of autonomous agent systems. It achieves this through a declarative agent specification, hierarchical memory, built-in safety mechanisms, and runtime optimizations for improved reliability and performance.

CORE is a novel framework that orchestrates collaborative Large Language Model (LLM) agents across hierarchical 6G edge networks to enable ubiquitous intelligence. It addresses the challenges of fragmented resources by integrating real-time perception, dynamic role orchestration, and pipeline-parallel execution, significantly enhancing system efficiency and task completion in various 6G applications.

This paper introduces a universal and versatile interface for controlling drones using large language models (LLMs) via the new Model Context Protocol (MCP) standard. It enables LLMs to command both real and simulated drones, dynamically integrating real-time situational data like maps for complex missions.