AGI

What if AI agents could imagine their future before taking a step—just like we do? That’s the vision behind SimuRA, a new architecture that pushes LLM-based agents beyond reactive decision-making and into the realm of internal deliberation. Introduced in the paper “SimuRA: Towards General Goal-Oriented Agent via Simulative Reasoning Architecture with LLM-Based World Model”, SimuRA’s key innovation lies in separating what might happen from what should be done. Instead of acting step-by-step based solely on observations, SimuRA-based agents simulate multiple futures using a learned world model and then reason over those hypothetical outcomes to pick the best action. This simple-sounding shift is surprisingly powerful—and may be a missing link in developing truly general AI. ...

When Ray Kurzweil first proposed the “Law of Accelerating Returns,” he suggested that technological progress builds on itself, speeding up over time. But what if even that framing is too slow? David Orban’s recent paper, Jolting Technologies: Superexponential Acceleration in AI Capabilities and Implications for AGI, pushes the discussion into new mathematical territory. Instead of modeling AI progress as exponential (where capability growth accelerates at a constant rate), he proposes something more radical: positive third-order derivatives — or in physics terms, jolts. ...

What will the future AI agent look like—a collection of specialized tools or a Swiss army knife of intelligence? As researchers and builders edge closer to Artificial General Intelligence (AGI), the design and structure of multi-domain agents becomes both a technical and economic question. Recent proposals like NGENT1 highlight a clear vision: agents that can simultaneously perceive, plan, act, and learn across text, vision, robotics, emotion, and decision-making. But is this convergence inevitable—or even desirable? ...