This paper presents MASⁿ, an N-dimensional geometric cognitive architecture for multi-agent AI systems. Building on the foundational MAS³ 3×3×3 structure (Role × Domain × Layer), MASⁿ extends the framework to include temporal reasoning, spatial context awareness, and recursive fractal decomposition. Key contributions:- MAS³ foundation: Formal 3×3×3 agent space definition with cross-dimensional verification- MASⁿ extensions: N-dimensional framework incorporating time, space, and recursion- Fibonacci-constrained scaling: Theorem 1 proves growth properties (not claimed as novel mathematics)- Evaluation protocol: Comprehensive 750-task benchmark with justified baselines- Honest scope: No implementation exists; all claims are theoretical and exploratory This version (v2.3) underwent rigorous multi-agent adversarial review and corrects overclaims present in v1.0. For focused introduction to MAS³ foundation, see companion paper: Cohen, E. (2025). MAS³: A Geometric Cognitive Architecture for Multi-Agent AI Systems.

Theoretical framework paper. Empirical validation in progress. Patent-pending architecture with implementation details confidential pending full patent examination.