SILENCIUM: A Pre-Inference De-Escalation and Intent-Gating Framework for LLM Interfaces (including Technical Addendum on Quantitative Drift Measurement)

Abstract: This repository contains the complete SILENCIUM framework, comprising the conceptual White Paper (Part I) and the Technical Addendum (Part II). SILENCIUM is an innovative approach to Interaction Governance in Large Language Model (LLM) interfaces. It addresses the challenges of escalation dynamics and manipulative exploitation of AI systems by introducing a Pre-Inference De-Escalation and Intent-Gating framework. Core Components of the Framework: Intent-Gating: A mechanism to filter user queries based on the identification of rhetorical pressure and emotional manipulation, preventing unnecessary inference costs and boundary violations. De-Escalation Policy: Strategies for the professional limitation of system responses when boundaries are crossed, inspired by proven de-escalation techniques from high-stress environments. Quantitative Drift Measurement (Part II): Formal mathematical definitions and metrics for measuring Semantic Drift and Epistemic Drift. It provides a methodology to quantify the divergence between user intent, system instructions, and factual grounding (Composite Risk Score). This framework is designed for developers, safety researchers, and AI infrastructure providers seeking to implement more stable, cost-effective, and secure interaction designs.