Enesetäpsust

Enesetäpsust is a concept used in epistemic and speculative discussions to denote the process by which an agent refines its own assertions, models, or plans through self-directed evaluation with the aim of increasing precision. The term is constructed from elements meaning “self” and “clarification” or “specification.”

Definition and scope. Enesetäpsust refers to an iterative cycle in which an agent identifies ambiguities or inconsistencies in its own output, generates corrected content or updated models, and re-evaluates them against evidence or criteria. The emphasis is on internal correction rather than external revision, and the process can operate at multiple levels, from language and argumentation to belief systems and methodological frameworks.

Mechanisms. A typical self-precision cycle includes: self-assessment, anomaly or error detection, generation of refined statements or models, internal validation against benchmarks, and adoption of revised outputs. In humans, this can manifest as metacognitive reflection; in machines, it can be implemented through self-critique modules, self-editing prompts, or internal simulation and testing.

Applications and context. In artificial intelligence, enesetäpsust is discussed as a design principle for self-improving systems, aimed at achieving greater accuracy and corrigibility while avoiding infinite loops. In education or scientific writing, it is invoked as a practice of clarity and careful revision. The concept sits alongside meta-cognition, self-correction, and self-editing, offering a framework for understanding how agents can tighten their own discourse and models.

Notes. The term is not widely standardized in established literature and appears mainly in theoretical or speculative contexts about self-directed reasoning and improvement. See also: meta-cognition, self-correction, self-editing.