coeren

Coeren is a conceptual term used in information theory and cognitive science to denote the property of a system, text, or process to maintain coherence across transformations and contexts. It describes how meaning, structure, and interpretability are preserved when data are translated, compressed, or queried.

The term is a neologism coined in academic discussions during the 2020s to articulate a measurable sense of coherence that goes beyond traditional notions of correctness or accuracy. It is employed across disciplines to capture the integrity of information flow and the persistence of interpretability despite changes in representation, modality, or scale.

Formal aspects of coeren involve a coherence score that combines semantic similarity, logical consistency, and contextual alignment. It assesses both local coherence within a passage and global coherence across a data pipeline. Methods proposed include coherence-penalized learning, graph-based consistency checks, and human-in-the-loop evaluation to balance automated metrics with subjective judgment.

Applications of coeren span several fields. In natural language processing, it informs evaluation of translations, summaries, and conversational agents by emphasizing sustained meaning and structure. In data science and software engineering, it guides data integration, ETL pipelines, and the reliability of state changes in distributed systems, where preserving interpretation across steps is important.

Limitations and debate exist around coeren, as its precise definition and computation vary by domain. Critics note that coherence is partly subjective and highly dependent on context, making universal metrics challenging. See also: coherence, coherence theory, information theory.