The Self-and-Mutual-and-World Model: A General Computational Framework for Adaptive Coordination Between Autonomous Computation, Interactive Mechanisms, and World-State Modeling

Computational systems operating in dynamic environments must coordinate their internal processing, interactions with other computational entities, and representations of the external world. Autonomous computation enables a system to process information and modify its own outputs without requiring continuous interaction with other entities. Mutual computation enables multiple entities to exchange information, evaluate alternatives, coordinate actions, and integrate complementary knowledge. World modeling provides representations of environmental states, transition dynamics, observations, and predicted consequences of possible actions. Although these three capabilities are individually important, their coordination is often addressed through separate architectural decisions or task-specific procedures.

This paper proposes the Self-and-Mutual-and-World Model (SMWM), a general computational framework for adaptively coordinating autonomous computation, interactive mechanisms, and world-state modeling. The framework treats self-mechanisms, mutual mechanisms, and world-model operations as three configurable but interdependent action spaces. A state-aware controller selects among autonomous processing, collaborative interaction, world-model updating, and coordinated combinations of these operations according to task requirements, uncertainty, environmental change, prediction error, interaction benefit, model reliability, and computational cost. The framework supports transitions not only between autonomous and interactive computation but also between different world-model operations, including state estimation, prediction, counterfactual simulation, planning, and model revision. A formal problem definition, utility function, adaptive control procedure, implementation architecture, research hypotheses, and experimental evaluation protocol are presented. The framework is a proposed conceptual and methodological design rather than an empirically validated system. Its intended contribution is to establish a common formulation for studying how computational autonomy, interaction, and world modeling can be jointly coordinated, while recognizing that autonomous agents, multi-agent coordination, predictive world models, and adaptive model-based control have substantial existing research foundations. Empirical investigation is required to determine whether the proposed integration provides measurable advantages over established architectures.