Modular Speaker Architecture: A Framework for Sustaining Responsibility and Contextual Integrity in Multi-Agent AI Communication

2025-06-01

AI Summary

In multi-agent AI communication, ambiguous speaker responsibility frequently causes role confusion, context drift, and unstable alignment, undermining interpretability and reliability. To address this, we propose a dual-mechanism framework: responsibility chain tracing and context integrity verification—enabling role-aware modeling, continuous responsibility flow propagation, and consistent context maintenance. We introduce structured evaluation metrics—pragmatic consistency, responsibility flow completeness rate, and context stability—and develop a lightweight G-Code configuration language with modular APIs to support high-context human-AI dialogue modeling. Our approach integrates role modeling, state tracking, and structured validation, leveraging human annotation, automated scoring, and bootstrapped statistical analysis. Experiments demonstrate a 42% improvement in responsibility flow completeness and a 37% gain in context stability, significantly enhancing structural robustness in multi-turn interactions—without reliance on affective signals or surface-level heuristics.

Technology Category

• Multiagent Systems: Agent Communication
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Sustaining role-aware communication in multi-agent AI systems
• Preventing context drift and alignment instability in dialogues
• Ensuring speaker responsibility and contextual coherence over time

Innovation

• Modular Speaker Architecture for multi-agent communication
• Role tracking and responsibility continuity modules
• Contextual Integrity Validator ensures coherence