Semantic Communication meets System 2 ML: How Abstraction, Compositionality and Emergent Languages Shape Intelligence

TL;DR

This paper advocates for a paradigm shift towards semantic communication inspired by System-2 cognition, emphasizing abstraction, compositionality, and emergent languages to develop more intelligent, adaptable, and goal-oriented systems.

Contribution

It introduces a unified research framework combining principles of System-2 cognition with communication, machine learning, and robotics for advanced intelligent systems.

Findings

Proposes a new paradigm integrating semantic understanding into communication systems.

Highlights the importance of abstraction, compositionality, and emergent languages for intelligence.

Lays theoretical groundwork for future systems capable of reasoning and collaboration.

Abstract

The trajectories of 6G and AI are set for a creative collision. However, current visions for 6G remain largely incremental evolutions of 5G, while progress in AI is hampered by brittle, data-hungry models that lack robust reasoning capabilities. This paper argues for a foundational paradigm shift, moving beyond the purely technical level of communication toward systems capable of semantic understanding and effective, goal-oriented interaction. We propose a unified research vision rooted in the principles of System-2 cognition, built upon three pillars: Abstraction, enabling agents to learn meaningful world models from raw sensorimotor data; Compositionality, providing the algebraic tools to combine learned concepts and subsystems; and Emergent Communication, allowing intelligent agents to create their own adaptive and grounded languages. By integrating these principles, we lay the groundwork for truly intelligent systems that can reason, adapt, and collaborate, unifying advances in wireless communications, machine learning, and robotics under a single coherent framework.