让AI对话系统保持角色清晰、责任连续，避免沟通跑偏。
Modular Speaker Architecture: A Framework for Sustaining Responsibility and Contextual Integrity in Multi-Agent AI Communication
- 将说话者行为拆解为角色追踪、责任链、上下文验证三个模块。
- 实验显示其在无情感信号下仍能稳定维持对话结构。
- 适合需要长期协作的多智能体系统，如客服机器人集群。
在多智能体系统中持续保持连贯、具备角色意识的通信仍是核心挑战。现有框架缺乏显式的说话者责任机制，导致上下文漂移、对齐不稳和可解释性下降。我们提出模块化说话者架构（MSA），将说话者行为分解为角色追踪、责任连续性和上下文一致性三个模块。基于高上下文的人机对话，MSA包含三个核心组件：说话者角色模块、责任链追踪器和上下文完整性验证器。通过标注案例研究评估，引入结构化指标——语用一致性、责任流和上下文稳定性，采用人工与自动评分结合及自助统计分析。结果表明，MSA能在不依赖情感信号或表面启发式规则的情况下可靠维持交互结构。我们进一步开发了原型配置语言（G-Code）和模块化API，支持在动态多智能体场景中的部署。
原文摘要 · Abstract (English)
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.
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