Dynamic Ambiguity Management Engine (DAME v1.0)/ Elastic Intent Resolution Protocol (EIRP): A Cognitive Safety Architecture for Adaptive Ambiguity Resolution and AI Accountability

Existing LLM architectures remain brittle when confronted with unclear, conflicting, or contextually drifting user inputs, often resulting in misinterpretation, user frustration, or ungrounded hallucinations. This paper introduces the Dynamic Ambiguity Management Engine (DAME v1.0), a novel cognitive control architecture that formalizes cognitive elasticity through its governing Elastic Intent Resolution Protocol (EIRP). DAME integrates real-time intent elasticity calibration, dynamic confidence-based response modulation, and proactive clarification-seeking pathways to robustly manage conversational uncertainty. We present a fully modular architecture—comprising the Input Processing Layer, Contextual Intent Representation Module, Ambiguity Detection Unit, Elasticity Tuning Supervisor, Clarification Seeking Engine, Confidence-Based Response Control, Dialogue State Manager, and Feedback Integration Loop. Building upon this, we introduce a comprehensive hierarchical failure mode taxonomy, layered containment protocols for risk mitigation, detailed emergent behavior analysis, and a multi-band system validation framework. The architecture is further governed by an operational deployment readiness framework that integrates safety thresholds, auditability, and continuous post-deployment cognitive governance. Collectively, DAME v1.0 / EIRP offers a scalable blueprint for constructing resilient, self-regulating, and governable AI systems capable of adaptive ambiguity resolution with transparent cognitive integrity across diverse domains of human-AI interaction.