arXiv:2606.05605v1 Announce Type: new Abstract: How does a system that merely predicts the world come to distinguish its own causal influence from everything else? We trace this transition in a minimal 192-dimensional GRU through 40 controlled experiments arranged as a developmental sequence, adding components one at a time and tracking whether the system can distinguish self-caused from world-caused changes. The developmental path reveals four conditions that must be satisfied in strict order: (1) persistent state forming stable attractors, (2) a causal action loop linking output to input, (3
This research provides a foundational understanding of how agency might emerge in AI, building on recent advances in neural network architectures and a growing focus on autonomous systems.
Understanding the developmental conditions for agency in minimal neural systems is crucial for designing and controlling more sophisticated AI, impacting future AI safety and capability.
This research shifts from purely predictive AI models to exploring architectures capable of distinguishing self-caused from world-caused changes, a key step towards true agency.
- · AI researchers
- · AI ethics and safety organizations
- · Developers of autonomous systems
- · Theories of agency not accounting for emergent properties
- · AI developers focused solely on predictive modeling
Further research will be directed at more complex systems and the environmental factors influencing agency development.
This understanding could lead to the creation of more robust and adaptive AI agents with enhanced self-preservation or goal-seeking behaviors.
The implications for human-AI interaction and the legal-philosophical status of advanced AI could become a significant societal debate.
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