Agentification of Nuclear Sensing Domains for Collective Spectral Reasoning

Modern machine learning methods have substantially advanced sensing by enabling increasingly sophisticated interpretation of acquired signals. Yet most existing approaches represent a sensing domain as a single computational object, producing monolithic vectors or sequences while leaving decomposition of the domain implicit. I introduce agentification, a learning paradigm in which a structured sensing domain is explicitly decomposed into localized subdomains and each subdomain is instantiated as a computational agent with explicit responsibility for local observation, learning, inference, and, optionally, interaction. Agentification therefore goes beyond partitioning by transforming domain components into computational entities that independently learn local behavior and contribute locally generated evidence to collective inference. I instantiate this paradigm in gamma-ray spectroscopy by transforming a 1,024-channel spectrum into 1,024 spectral agents, each using a Gaussian process to learn normal background behavior and quantify anomalous local observations. This setting targets a central nuclear-security challenge: weak source-induced counts can be masked by naturally fluctuating radiation background. The detector is source agnostic: Cs-137 and Co-60 response functions are used only to generate controlled evaluation anomalies and are never supplied to the inference framework. Across ten repeated 70/30 holdouts, agentification improves source-agnostic AUC over PCA, a nonlinear autoencoder, and the strongest monolithic baseline in the weakest source regime, while the advantage diminishes or reverses as anomalies become stronger. Agent-level evidence also provides intrinsic spectral localization, with channel-level localization AUCs of 0.718/0.789 for Cs-137/Co-60 at the weakest tested level and 0.842/0.883 at the strongest. Under partial sensing-domain degradation, agentification remains competitive and can reverse the intact-domain ordering under substantial contiguous channel loss. Simple neighboring-agent communication is not consistently beneficial, demonstrating that agent formation and communication are distinct design dimensions. These results establish agentification as a general approach for transforming structured sensing observations into populations of locally responsible computational entities and provide a computational foundation for Agentic Nuclear Sensing Systems (ANSS).