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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26Cited by 0

Narsi Regression and Narsi Intelligence: A Unified Theoretical Framework for Dynamic Representation Evolution, Recursive Cognitive Adaptation, and Self-Evolving Artificial Intelligence

A Chaudhary

Narsi Regression is a theoretical framework that extends conventional machine learning by treating representation evolution as an explicit optimisation problem rather than an implicit consequence of parameter optimisation. The framework models a learner as a dynamic state consisting of predictive parameters, representations, memory, and adaptive policies. It introduces an energy-based optimisation framework, representation mutation operators, recursive adaptation mechanisms, and a mathematical formulation for continual representation evolution. Narsi Intelligence extends this formulation by allowing the adaptation policy itself to evolve under controlled constraints. The work unifies concepts from representation learning, continual learning, meta-learning, memory-augmented learning, neural architecture search, neuroevolution, and recursive self-improvement within a single mathematical framework. This publication presents a theoretical foundation intended for future algorithmic implementation and empirical evaluation.Artificial Intelligence

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