Artificial Agency Program: Curiosity, compression, and communication in agents

TL;DR

The paper proposes the Artificial Agency Program, a research framework for developing resource-bounded AI agents driven by curiosity, integrating information theory, intrinsic motivation, and communication within a unified experimental platform.

Contribution

It introduces a comprehensive research agenda unifying predictive compression, intrinsic motivation, and communication as information bottlenecks for resource-bounded AI agents.

Findings

Develops a falsifiable program with explicit costs and staged experiments.

Creates a multimodal tokenized testbed for agent development.

Connects intrinsic motivation with information theory and thermodynamics.

Abstract

This paper presents the Artificial Agency Program (AAP), a position and research agenda for building AI systems as reality embedded, resource-bounded agents whose development is driven by curiosity-as-learning-progress under physical and computational constraints. The central thesis is that AI is most useful when treated as part of an extended human--tool system that increases sensing, understanding, and actuation capability while reducing friction at the interface between people, tools, and environments. The agenda unifies predictive compression, intrinsic motivation, empowerment and control, interface quality (unification), and language/self-communication as selective information bottlenecks. We formulate these ideas as a falsifiable program with explicit costs, staged experiments, and a concrete multimodal tokenized testbed in which an agent allocates limited budget among observation, action, and deliberation. The aim is to provide a conceptual and experimental framework that connects intrinsic motivation, information theory, thermodynamics, bounded rationality, and modern reasoning systems