"Theater of Mind" for LLMs: A Cognitive Architecture Based on Global Workspace Theory
Wenlong Shang

TL;DR
This paper introduces Global Workspace Agents, a novel cognitive architecture inspired by Global Workspace Theory, enabling autonomous, continuous, and self-regulating operation of large language models.
Contribution
It proposes a dynamic, event-driven multi-agent framework with entropy-based regulation and memory strategies to overcome passive, reactive limitations of current LLMs.
Findings
GWA maintains continuous cognitive cycles in LLMs.
Entropy-based drive regulates semantic diversity and reasoning flow.
Dual-layer memory ensures long-term cognitive continuity.
Abstract
Modern Large Language Models (LLMs) operate fundamentally as Bounded-Input Bounded-Output (BIBO) systems. They remain in a passive state until explicitly prompted, computing localized responses without intrinsic temporal continuity. While effective for isolated tasks, this reactive paradigm presents a critical bottleneck for engineering autonomous artificial intelligence. Current multi-agent frameworks attempt to distribute cognitive load but frequently rely on static memory pools and passive message passing, which inevitably leads to cognitive stagnation and homogeneous deadlocks during extended execution. To address this structural limitation, we propose Global Workspace Agents (GWA), a cognitive architecture inspired by Global Workspace Theory. GWA transitions multi-agent coordination from a passive data structure to an active, event-driven discrete dynamical system. By coupling a…
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