Noesis Tension: A Telemetry-Driven Taxonomy of Prompt-Induced Representational Pressures in Large Language Models

Noesis Tension is a lightweight, strictly telemetry-only framework for diagnosing how prompts induce specific representational pressures inside large language models. Using only per-layer activation statistics (mean/median/p95 deltas, curvature, entropy, and final-layer spikes), the system computes two core indices (tension and drift) and maps them onto a compact set of twelve empirically-derived “tension categories. ” These categories are then used by a deterministic classifier to assign high-level cognitive regimes (e. g. , exploratoryₗiminal, confidentₕallucinationₗite, safetyₚrocedural). The framework requires no inspection of prompt or response text, runs in microseconds, and shows stable core attractors across architecturally distinct models (Llama-3. 1-8B, Mistral-7B, Qwen1. 5-MoE-2. 7B). Results are demonstrated on a false-presupposition prompt that reliably triggers ontological impossibility and related pressures. This work constitutes Phase III of the NOESIS project and builds directly on the conceptual foundations established in: - Phase I: Toward Epistemic Regime Detection (Zenodo, Dec 2025) - Phase II: Observability Architecture for Cognitive Telemetry (Zenodo, Jan 2026) Full classifier source code and example traces are included in the appendix of the preprint and will be open-sourced upon arXiv submission. Author: James Benjamin Jones (ORCID: 0009-0002-6129-2847)