MBZUAI
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@mbzuai
Official account of Mohamed bin Zayed University of Artificial Intelligence. Dedicated to research, innovation, and empowering brilliant minds in AI.
With the rise of LLM systems marketed as "coding agents", "AI co-scientists", etc. that promise to drive up productivity, and at the same time outcry of "existential" concerns that AI escaping human control with destructive power under a speculative "machine agency" against humans, there has been lots of confusion about “What is an agent?” and “What constitutes agency?” It has become essential to clarify where automation ends and agency begins. Also recently, developments in world models, action models are trending to mixing future prediction/simulation and action/plan generation altogether within a single architecture such as a VLM, conflating reward-driven action selection with fidelity-driven next-state prediction, undermining the reliability of both planning and simulation. In this paper we analyze agent architectures along the axis of goal, identity, decision-making, self-regulation, and learning, and argue that genuine agency requires these structures to be internalized within the system itself rather than assembled through external scaffolding. We propose a “Goal-Identity-Configurator” (GIC) architecture for a general-purpose agent model, combining hierarchical goal decomposition, identity evolution, simulative reasoning grounded in a separately trained world model, learned self-regulation, and self-directed learning from both real and simulated experience. Auditability, controllability, and safety of systems that possess greater autonomy and "agency” but remain under human oversight, can be better built with the GIC architecture that offers transparency, modularity, and checkpoints. @mdeng34 , @jinyuhou0 openreview.net/forum?id=6fDZY…
