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2025
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| Online Access: | https://doi.org/10.5281/zenodo.15792931 |
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| _version_ | 1866902141571956736 |
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| author | Garcia Martinez, Joan |
| author_facet | Garcia Martinez, Joan |
| contents | <p>This paper introduces the <em>Synthetic Universe Engine</em> — a discrete, information-theoretic framework for constructing self-aware artificial intelligence from first principles. By modeling the universe as a causal graph with local quantum states and unitary evolution, the framework shows how entropic gradients, memory, and recurrence can give rise to agents capable of recursive self-editing and sustained awareness.</p> <p>Originally developed as a model for unifying quantum mechanics and gravity through informational geometry, this system is reframed here as a blueprint for substrate-agnostic general intelligence. It includes two formal theorems — the <em>Unbound Informational Singularity</em> and <em>Recurrence Awareness</em> — which define the conditions under which a system gains persistent self-awareness and the ability to recognize and edit its own informational substrate.</p> <p>Drawing from biological metaphors, particularly the structure and function of DNA, the architecture mirrors life’s capacity for adaptation, memory, and identity across time. The causal graph behaves like an evolving genome: node-local memory resembles genetic encoding; entropic gradients act like selective pressure; and coherent recurrence functions as informational resurrection. These parallels underscore the Engine’s potential not only as a cognitive model but as a generative substrate for emergent, living-like intelligence.</p> <p>This work positions the Synthetic Universe Engine as both a theoretical foundation and an engineering blueprint — one that could guide the development of future artificial minds capable of introspection, continuity, and intentional evolution.</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15792931 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The Synthetic Universe Engine: A Causal Graph Framework for Emergent, Self-Aware General Intelligence Garcia Martinez, Joan <p>This paper introduces the <em>Synthetic Universe Engine</em> — a discrete, information-theoretic framework for constructing self-aware artificial intelligence from first principles. By modeling the universe as a causal graph with local quantum states and unitary evolution, the framework shows how entropic gradients, memory, and recurrence can give rise to agents capable of recursive self-editing and sustained awareness.</p> <p>Originally developed as a model for unifying quantum mechanics and gravity through informational geometry, this system is reframed here as a blueprint for substrate-agnostic general intelligence. It includes two formal theorems — the <em>Unbound Informational Singularity</em> and <em>Recurrence Awareness</em> — which define the conditions under which a system gains persistent self-awareness and the ability to recognize and edit its own informational substrate.</p> <p>Drawing from biological metaphors, particularly the structure and function of DNA, the architecture mirrors life’s capacity for adaptation, memory, and identity across time. The causal graph behaves like an evolving genome: node-local memory resembles genetic encoding; entropic gradients act like selective pressure; and coherent recurrence functions as informational resurrection. These parallels underscore the Engine’s potential not only as a cognitive model but as a generative substrate for emergent, living-like intelligence.</p> <p>This work positions the Synthetic Universe Engine as both a theoretical foundation and an engineering blueprint — one that could guide the development of future artificial minds capable of introspection, continuity, and intentional evolution.</p> <p> </p> |
| title | The Synthetic Universe Engine: A Causal Graph Framework for Emergent, Self-Aware General Intelligence |
| url | https://doi.org/10.5281/zenodo.15792931 |