Integrasi Pembelajaran Penguatan Terkonstran, Pemodelan Dunia, dan Bio-Energetika dalam Paradigma Kecerdasan Buatan 2026

The development of artificial intelligence (AI) has entered a critical transition phase since early 2026, moving from the era of large-scale generative model experimentation to the implementation of autonomous agent systems that must operate within stringent physical, energy, and ethical constraints. The global research focus, as reflected in major conferences such as NeurIPS and ICLR, is no longer simply pursuing parameter scaling or FLOPS (floating-point operations per second), but has shifted toward systemic efficiency, functional reliability, and biologically inspired metabolic tuning. A key challenge facing the scientific community today is how to build machines that are not only cognitively intelligent but also capable of justifying every unit of energy consumed and every operational risk taken in uncertain and often hostile environments. This research report presents a comprehensive analysis of the convergence of novel optimization methods, policy-aware world modeling, and the "Green AI" paradigm, which positions sustainability as a first-class design metric in the development of future autonomous intelligence.