Simulation of Language Evolution under Regulated Social Media Platforms: A Synergistic Approach of Large Language Models and Genetic Algorithms

2026-06-19 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A multi-agent framework combining Large Language Models (LLMs) and Genetic Algorithms (GAs) simulates language evolution under social media content moderation. This framework features participant agents, acting as users, who iteratively refine language strategies, and supervisory agents that emulate platform regulation by detecting policy violations. It employs a dual strategy design—constraint for evasion and expression for clarity—and uses an LLM-driven GA for strategy selection, mutation, and crossover. Evaluated across an abstract password game and a realistic illicit pet trade scenario, the system demonstrates that with increasing dialogue rounds, both uninterrupted dialogue turns and information transmission accuracy significantly improve. A user study with 40 participants validated the generated dialogues' real-world relevance, and ablation studies confirmed the GA's contribution to long-term adaptability.

Key takeaway

For AI Scientists and Machine Learning Engineers developing content moderation systems or social simulations, this LLM-GA framework provides a powerful tool to model adversarial language evolution. You can use this approach to proactively identify emerging evasion tactics and refine moderation policies, or to generate more realistic, nuanced social media dialogues. Consider fine-tuning LLMs with domain-specific data to better capture subcultural linguistic shifts and enhance simulation fidelity.

Key insights

The framework simulates adversarial language evolution on social media using LLM-driven multi-agent systems and Genetic Algorithms.

Principles

• Adversarial interaction drives language evolution under content moderation.
• Decomposing strategies into "constraint" and "expression" improves simulation.
• LLM-driven Genetic Algorithms enhance long-term strategy adaptability.

Method

The framework uses LLM-powered participant agents for reflection, planning, and dialogue, and a supervisory agent for content review. Language strategies evolve via LLM-operated GA (selection, crossover, mutation).

In practice

• Simulate covert communication tactics.
• Evaluate content moderation policy effectiveness.
• Generate euphemistic language for sensitive topics.

Topics

• Large Language Models
• Genetic Algorithms
• Multi-Agent Systems
• Language Evolution
• Social Media Moderation
• Adversarial AI

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.