Reflexivum

Reflexivum is a hypothetical construct used in philosophy of mind, cognitive science, and theoretical discussions of artificial intelligence. It refers to an idealized capacity of a system to model, assess, and revise its own internal states and processes. In this sense, Reflexivum functions as a meta-cognitive resource: a mechanism by which a system can turn its attention back on itself to improve alignment with goals, safety constraints, or changing environments.

The term Reflexivum is derived from the Latin reflexivus, meaning turning back, combined with a standard theoretical suffix. The concept does not denote a specific algorithm or a completed technology, but rather a family of properties that researchers may seek to approximate in models and architectures.

In formal discussions, Reflexivum is described as having two key features. First, self-representation: the system maintains an internal model of its own reasoning and states. Second, self-application: it can simulate the effects of modifying its own processes and evaluate potential outcomes. Together, these capabilities enable introspective reasoning about decisions, uncertainties, and goals. Reflexivum is not synonymous with general self-awareness or consciousness; it is a framework for introspective analysis that can be pursued in finite, well-defined ways within computational systems.

Applications are typically discussed in the contexts of AI safety, transparency, and reliable self-improvement. A Reflexivum-inspired module might help detect bias, assess uncertainty, or verify that changes to a model remain aligned with overarching objectives. The concept remains debated, with critics noting the lack of a precise operational definition and the risk of conflating descriptive reflexivity with prescriptive self-modification.

See also: reflexivity, self-reference, meta-cognition, introspection, artificial intelligence safety.