We propose Heuristic Physics as a foundational reconceptualization of physical law — not as ontological decree, but as semantic program. In this framework, classical laws such as Newton’s are reinterpreted as emergent heuristics: efficient symbolic compressions of deeper, probabilistic, and relational substrates. These laws persist not because they are fundamentally true, but because they are…

Modern computation remains dominated by the combinational paradigm — a design ethos in which outputs are computed solely as a function of current inputs, with no inherent continuity of state. Though contemporary applications simulate memory, context, and persistence, their foundations still rest upon von Neumann architectures, finite-state control, and procedural abstraction. What appears as a…

Artificial Intelligence (AI) has made remarkable strides in recent years, achieving feats of perception, reasoning, and decision-making once reserved for human cognition. However, even as AI systems gain sophistication, they remain fundamentally brittle when encountering internal failures, such as reasoning inconsistencies, model drift, or feedback anomalies. Traditional fault-tolerance mechanisms focus on hardware redundancy, software resets,…

This paper introduces a theoretically grounded and technically innovative Artificial General Intelligence (AGI) architecture, designed to integrate emotional and logical processes within synchronized feedback loops. The proposed Resonant Synchronization Framework (RSF) fuses affective computing modules with advanced cognitive reasoning, mediated by dynamic coupling and adaptive recalibration mechanisms. The resulting architecture endows AGI systems with the ability to…

This paper presents a novel architecture for artificial general intelligence (AGI) systems designed to harmonize conflicting objectives in real time under conditions of uncertainty. Drawing inspiration from orchestral metaphors and adaptive control theory, we propose a multi-layered goal-orchestration framework that enables AGI to prioritize, balance, and revise goals dynamically. Our approach integrates adaptive meta-controllers, hierarchical…

Artificial agents have traditionally been designed to reason through linear sequences, treating past as immutable input and future as a forecast. However, symbolic cognition in humans often violates this directionality, inferring causes from anticipated outcomes and modifying past interpretations based on new futures. This paper introduces nonlinear temporality in symbolic reasoning systems. We model cognitive…