An Overview of GenAI 2.0 Partnering With Digital Twin to Enhance Decision Making

Abstract

Generative AI (GenAI) has moved on to a “2.0” level, where capability comes not only from better generators but from integrating generators with retrieval, tool use, and orchestration. The approach taken in this overview is a systems perspective, where a brief typology of families and of models is codified, along with a distillation of design patterns on how bare generators can be refined into reliable systems, namely containerized RAG, tool schemas, eventdriven context, and conservative agent systems. In this position, we propose a practice-driven assessment perspective integrating task quality, along with Attribution, Robustness with respect to distribution shift & adversarial prompts, safety & governance (Privacy, IP, Provenance), and End-to-End Efficiency in Latency, Cost, & Energy. Text/Code, Vision-Language, Speech/Audio, Networks, Public Services, & Critical Infrastructure applications provide examples of these trends extending from benchmarking. The issues it brings to light include controllability and verifiability for long-range, multiple tool agents, scalable contamination aware assessment, traceability preserving data and model stewardship, as well as efficiency for resource-constrained and edge scenarios, while pointing towards near-term areas such as tool interface interoperability, traceability aware retrieval, lifecycle visibility, reporting card interoperability, and reliability assessment. Focusing on operational criteria, the research has contributed to creating a impact for making GenAI 2.0 Trustable AI. The blueprint can be utilized as a guide for future research.