ERS — Epistemic Relevance Space: Toward an Orientation-Centered Environment for Navigating AI-Driven Meaning Overproduction

This concept paper introduces ERS — Epistemic Relevance Space, an orientation-centered environment designed to address the structural limitations of current knowledge and AI systems under conditions of information overload and AI-driven overproduction of meaning. Contemporary systems, including knowledge graphs, note-taking tools, and large language models, are optimized for information organization, retrieval, or meaning generation. However, they do not support orientation as a primary cognitive function. As a result, increasing volumes of information and fluent AI-generated outputs do not enhance orientation but instead contribute to epistemic overload, premature meaning stabilization, and the emergence of epistemic pseudo-orientation. ERS proposes a shift from information-centric and meaning-centric systems toward an orientation-centered paradigm. The system is not designed to produce answers or stabilize meaning, but to regulate the conditions under which orientation becomes possible. The core principle of ERS is relevance-first processing. Instead of organizing information by static categories or semantic similarity, ERS operates on dynamic relevance signals that emerge in relation to the user’s current orientation field. These signals form temporary clusters that are continuously reconfigured through interaction. The system enables users to explore complex domains by manipulating clusters of relevance in a spatial environment. By bringing distant domains into proximity, users can trigger the emergence of previously hidden connections, intermediate paths, and structural bridges. ERS does not impose a single interpretation but instead provides multiple alternative stabilizations of meaning, allowing users to remain aware of the contingency of interpretation. A central design principle of ERS is the prevention of premature meaning closure. Under conditions of AI-generated fluency, meaning tends to stabilize too quickly, replacing orientation with coherent but potentially misleading narratives. ERS addresses this by introducing controlled multi-interpretation and by maintaining epistemic tension within the system. The goal is not to eliminate meaning but to delay and regulate its stabilization. ERS explicitly distinguishes between orientation, relevance, and meaning. Orientation is treated as a precondition for detecting and structuring relevance, while meaning is understood as a temporary stabilization emerging from clustered relevance under specific conditions. The system is conceived as an environment rather than a tool. It is designed for exploratory, non-linear interaction, allowing users to navigate complexity without predefined goals. In this sense, ERS supports trackness — directed exploration without a fixed telos — while maintaining the user’s epistemic agency. ERS does not aim to replace human judgment but to support decision-making by making relevance structures visible and by exposing the risks of premature closure, drift, and pseudo-orientation. It is therefore compatible with decision-oriented frameworks that emphasize traceability, ownership, and timing of decisions. By shifting the focus from knowledge organization to orientation regulation, ERS introduces a new class of epistemic systems. It responds to the structural conditions of a post-information environment in which meaning is abundant but orientation is scarce. Author keywords (free terms): Epistemic Relevance Space; orientation; relevance; trackness; premature closure; pseudo-orientation; AI overload; meaning overproduction; epistemic regulation; orientation-based inquiry. Internal reference: ERS₀1 (v0. 1)