arXiv:2606.15386v1 Announce Type: new Abstract: Open-ended intelligence is the capacity to adapt to novel problems and environments that are substantially different from those in training. We formalize open-ended intelligence as the closure induced by a finite primitive set \(P\) and a set of composition operators \(C\). We characterize properties of the induced closure \(\mathcal{L}(P,C)\) that support unbounded compositional generation across families of tasks and worlds. A mathematics of open-ended intelligence requires two pillars: a minimal set of representational primitives (e.g., states
The continuous advancements in AI research, particularly in foundational models, necessitate a theoretical framework to understand and achieve truly adaptive intelligence, moving beyond specific task-oriented training.
This framework is crucial for understanding and building AI systems capable of adapting to novel problems and environments, a key step towards general artificial intelligence and the future of autonomous systems.
This theoretical formalization provides a foundational schema for the research and development of AI, potentially shifting focus from mere performance metrics to compositional adaptability and emergent capabilities.
- · AI research institutions
- · Robotics developers
- · Autonomous systems sector
- · Generative AI companies
- · AI models without compositional adaptability
- · Narrow AI solution providers
- · Traditional algorithmic development
The development of AI systems capable of open-ended learning and adaptation will accelerate.
This acceleration could lead to AI agents that autonomously discover and contribute to novel scientific and engineering solutions.
A truly open-ended intelligence could fundamentally alter human-computer interaction and the nature of work across all industries.
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