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 introduces Learning-Infused Formal Reasoning (LIFR), a framework that integrates machine learning with formal verification to address the challenges of opaque AI systems and labor-intensive formal methods. LIFR aims to enable automated contract synthesis, semantic reuse of verification artifacts, and provide rigorous semantic foundations for scalable and trustworthy software engineering.

The paper argues that high‑stakes AI systems threaten human agency and proposes a Causal‑Agency Framework that embeds causal modeling, uncertainty quantification, and actionable interfaces to restore user control.

This paper introduces MemJack, a memory-augmented multi-agent framework designed to systematically expose visual-semantic vulnerabilities in Vision-Language Models (VLMs). It orchestrates automated jailbreak attacks using unmodified natural images by dynamically mapping visual entities to malicious intents and leverages a persistent memory to transfer successful strategies across different images.

This paper surveys artificial intelligence methods for modeling and simulating mixed automated and human traffic, addressing the limitations of existing simulation tools in accurately representing complex driving behaviors. It proposes a comprehensive taxonomy of AI methods, reviews evaluation protocols, and outlines future research directions to bridge the gap between transportation engineering and computer science.