Abstract spatial reasoning and dynamic environment adaptation pose significant challenges to contemporary Artificial General Intelligence (AGI) paradigms. Benchmark suites such as the Abstraction and Reasoning Corpus (ARC-AGI-2 and ARC-AGI3) demand rapid task acquisition from minimal training demonstrations under strict offline execution constraints. This paper proposes a unified framework integrating Digital Twin (DT) memory architectures, cognitive profiling, and Key Performance Indicator (KPI)-driven strategic governance to control adaptive candidate inference.
We introduce a dual-hypothesis prediction mechanism guided by dynamic experience search and mathematical KPI evaluation routines. Empirical evaluation on the ARC-AGI benchmarks demonstrates near-instant computational execution latency (≈ 0.41 ms per task on ARC-AGI-2), 100% valid payload serialization stability without runtime crashes, and high action-efficiency scores (> 96.50%) across dynamic environment domains, while explicitly delineating system execution throughput from exact problem-solving reasoning accuracy.

Biographical notes:
Trinh Quang Minh is a faculty member in the Faculty of Engineering & Technology at Tay Do University, Can Tho City, Viet Nam. His research interests include artificial general intelligence (AGI), cognitive models, digital twin systems, adaptive inference, and software engineering methodologies.Ngo Thi Lan is a faculty member in the Faculty of Engineering & Technology at Tay Do University, Can Tho City, Viet Nam. Her research interests focus on computer science, intelligent control systems, decision support frameworks, and applied machine learning.