arXiv:2605.14998v3 Announce Type: replace Abstract: From subcellular structures to entire organisms, many natural systems generate complex organisation through self-organisation: local interactions that collectively give rise to global structure without any blueprint of the outcome. Yet a significant portion of the information driving such processes is not produced by self-organisation itself, instead, it is often offloaded to initial conditions of the system. Biological development is a prime example, where maternal pre-patterns encode positional and symmetry-breaking information that scaffol
The paper represents a significant theoretical advancement in understanding how complex systems self-organize, bridging principles from biology to artificial intelligence.
This research provides a framework for designing AI systems that can achieve complex objectives through self-organization, rather than explicit programming, mimicking biological development.
The paradigm for developing advanced AI could shift from purely top-down design to incorporating biologically inspired scaffolding architectures for autonomous learning.
- · AI researchers
- · Robotics developers
- · Synthetic biology
- · Autonomous systems
- · Traditional algorithmic programming
- · Systems requiring extensive manual design
New AI architectures will emerge that are more robust and adaptive, capable of solving previously intractable problems.
This could lead to a faster path toward general AI by leveraging self-organization principles.
The application of such 'developmental AI' in areas like materials science or drug discovery could revolutionize scientific discovery and engineering.
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Read at arXiv cs.AI