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Evidence record 7266 · automatically gathered

LLM Constitutional Multi-Agent Governance

Large Language Models (LLMs) can generate persuasive influence strategies that shift cooperative behavior in multi-agent populations, but a critical question remains: does the resulting cooperation reflect genuine prosocial alignment, or does it mask erosion of agent autonomy, epistemic integrity, and distributional fairness? We introduce Constitutional Multi-Agent Governance (CMAG), a two-stage framework that interposes between an LLM policy compiler and a networked agent population, combining

Record details

Published: 13 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation · Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (13 March 2026), “LLM Constitutional Multi-Agent Governance,” evidence record 7266, https://ethics.ai/record/7266 (originally published by arXiv).

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