Abstract
This paper introduces Context-Sovereign Hypergraph Meshe, a next-generation computational substrate designed to replace static knowledge representations and linear context aggregation systems with continuously evolving, causally grounded, multi-agent decision topology. Unlike prior systems that model information as either retrieval artifacts or relational graphs, CSHM encodes decision reality as a living, self-updating hypergraph of probabilistic intent, constraint propagation, and irreversible state transitions.
This is designed to resolve three structural limitations in current AI-native enterprise systems:
- Context collapse under high-dimensional retrieval
- Loss of causal traceability in multi-agent workflows
- Static system-of-record boundaries incompatible with adaptive autonomy
The proposed architecture enables persistent, self-healing knowledge-state environments in which computation, memory, and governance are unified into a single substrate.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Manuel-Devadoss, Johny and Prasad, Deepthi, "Context-Sovereign Hypergraph Meshes for Autonomous Socio-Computational Systems", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10017