NOESE Instrumented I3A Architecture for Deterministic Computation and Self Evolving Agent Genesis at Inference Time

Abstract

Contemporary artificial intelligence systems are predominantly based on large monolithic models optimized for statistical performance and scale. While effective for many tasks, such architectures exhibit structural limitations when requirements of interpretability, deterministic computation, role specialization, and controlled system evolution arise. This work introduces **Noese**, an instrumented I3A (Intelligent Artificial Autonomous Agents) architecture designed to address these limitations through calibrated multi-agent organization, explicit role contracts, and fully traceable inference-time execution. In Noese, intelligence emerges from the coordination of specialized agents operating under strict constraints, rather than from a single opaque model. All agent interactions are logged, measured, and auditable, enabling systematic analysis and reproducibility. A central contribution of this work is a model compatibility and calibration framework that evaluates heterogeneous AI models and assigns them to agent roles based on operational criteria such as stability, instruction discipline, numerical precision, constraint compliance, and resource cost. This transforms model selection into a measurable engineering step rather than an empirical heuristic. Deterministic computation within a generative system is demonstrated through AURELION, a dedicated mathematical agent operating under strict procedural constraints. Across multiple experiments, AURELION produces reproducible numerical outputs verified against reference implementations, showing that deterministic reasoning can be embedded within an AI-driven multi-agent architecture. Beyond computation, NOÈSE supports inference-time self-evolution. When cognitive gaps are detected during execution, the system triggers controlled generation of new specialized agents. Large-scale experiments show bounded and non-random agent genesis, while a controlled replay study demonstrates that small algorithmic modifications can selectively suppress or enable agent creation without collapsing internal dynamics. Taken together, these results position NOÈSE as a concrete step toward instrumented, deterministic, and selfevolving artificial intelligence architectures, where emergence is an observable and constrained process rather than a metaphor. The framework outlines a viable path toward robust, non-anthropomorphic artificial intelligence systems grounded in engineering principles and empirical verification.

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