Abstract
We propose Recursive Constraint Topology Engines (RCTE), a class of intelligence systems in which cognition emerges not from agents, policies, or explicit reward optimization, but from continuously deforming constraint manifolds in a high-dimensional semantic phase space. Unlike agent-based systems, RCTE replaces "actors" with constraint fields that self-interact, self-negate, and self-stabilize through topological recursion.
The key hypothesis is that general intelligence arises when a system ceases to represent knowledge as symbolic objects or policies, and instead operates entirely on evolving inconsistency surfaces whose geometry encodes reasoning itself.
This architecture is designed to:
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eliminate discrete agent decomposition
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remove centralized planners and shared memory bottlenecks
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enable emergent abstraction without explicit training labels
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support continual self-rewriting logic under stability constraints
We formalize the system, derive its computational properties, and describe scaling laws under ASI regimes.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Manuel-Devadoss, Johny and Prasad, Deepthi, "Recursive Constraint Topology Engines: A Non-Canonical Architecture for Scalable General Intelligence Beyond Agentic Paradigms", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10018