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

arXiv: https://arxiv.org/abs/2506.01095 · HTML: https://mlsanity.com/paper/2506.01095/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 1 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [Language Models as Agent Models](https://mlsanity.com/paper/2212.01681/) (2022)
- [Influencing Towards Stable Multi-Agent Interactions](https://mlsanity.com/paper/2110.08229/) (2021)
- [Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models](https://mlsanity.com/paper/2407.12532/) (2024)
- [Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions](https://mlsanity.com/paper/2410.07409/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures](https://mlsanity.com/paper/2506.19676/) (2025)

## Abstract (arXiv)

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.