Modular Speaker Architecture: A Framework for Sustaining Responsibility and Contextual Integrity in Multi-Agent AI Communication
This addresses the problem of context drift and alignment instability in multi-agent AI communication, offering a framework for improved interpretability and responsibility, though it appears incremental as it builds on existing modular approaches.
The paper tackles the challenge of sustaining coherent, role-aware communication in multi-agent AI systems by proposing the Modular Speaker Architecture (MSA), which decomposes speaker behavior into modules for role tracking, responsibility continuity, and contextual coherence, and shows that MSA reliably maintains interaction structure through annotated case studies and structural metrics.
Sustaining coherent, role-aware communication across multi-agent systems remains a foundational challenge in AI. Current frameworks often lack explicit mechanisms for speaker responsibility, leading to context drift, alignment instability, and degraded interpretability over time. We propose the Modular Speaker Architecture (MSA), a framework that decomposes speaker behavior into modular components for role tracking, responsibility continuity, and contextual coherence. Grounded in high-context human-AI dialogues, MSA includes three core modules: a Speaker Role Module, a Responsibility Chain Tracker, and a Contextual Integrity Validator. We evaluate MSA through annotated case studies and introduce structural metrics-pragmatic consistency, responsibility flow, and context stability-quantified via manual and automatic scoring and bootstrapped statistical analysis. Our results show that MSA reliably maintains interaction structure without reliance on affective signals or surface-level heuristics. We further implement a prototype configuration language (G-Code) and modular API to support MSA deployment in dynamic multi-agent scenarios.