The research and development (R&D) of intelligent virtual agents (IVAs) is inherently complex.We aim to manage this complexity by releasing a major update to the Virtual Human Toolkit, combining the best aspects of academic and commercial approaches into a principled R&D platform that emphasizes interoperability, extendability, re-use, and support for multiple hardware targets.We demonstrate the current status of the VHToolkit on Windows desktop and Quest 3 AR/VR headset.

Division, square root, and remainder are fundamental operations required by most computer systems. Floating-point and integer operations are commonly performed on separate datapaths. This paper presents the first detailed implementation of a shared recurrence unit that supports floating-point division/square root and integer division/remainder. It supports early termination and shares the normalization shifter needed for integer and subnormal inputs. Synthesis results show that shared double-precision dividers producing at least 4 bits per cycle are 9 - 18% smaller and 3 - 16% faster than separate integer and floating-point units.

The research and development (R&D) of intelligent virtual agents (IVAs) is inherently complex. We aim to manage this complexity by combining the best aspects of academic and commercial approaches into a principled R&D platform that emphasizes interoperability, ex-tendability, re-use, and support for multiple hardware targets. This IVA platform, the Virtual Human Toolkit 2.0, is a re-architecture of our earlier work and combines a modular message passing architecture with that of a microservices architecture. This paper discusses our approach, design decisions, lessons learned, and current status of this ongoing effort. We illustrate the strengths of the architecture, how best to use commodity AI cloud services in one's own work, and how to port legacy stand-alone software to a web service.