The paper introduces a comprehensive method and ecosystem (NexAU, NexA4A, NexGAP) to overcome limitations in scaling interactive environments for training agentic Large Language Models (LLMs). This infrastructure enables the systematic generation of diverse, complex, and realistically grounded interaction trajectories for LLMs.

This paper introduces a logic programming-based framework for autonomous agents to reason about potential penalties for non-compliance with policies. It enables agents to achieve high-stakes goals by identifying optimal non-compliant plans that minimize repercussions, and also assists policymakers by simulating human decision-making under policy constraints.