# LLM Drift: A Study on Behavioral Drift Phenomenon of Large Language Models in Adversarial Multi-Agent Interactions

> This article introduces a research platform for quantifying the LLM Drift phenomenon. The platform uses LangGraph to build adversarial debate simulations and combines 22-dimensional behavioral metrics to evaluate the drift of models across five dimensions—psychometrics, personality traits, emotional states, cognitive structure, and social relationships—during long-term interactions.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-02T21:41:10.000Z
- 最近活动: 2026-05-02T21:48:26.392Z
- 热度: 0.0
- 关键词: LLM Drift, 大语言模型, 行为漂移, 多智能体系统, LangGraph, 对抗性交互, 人格一致性, AI安全, RAGAS评估, Streamlit可视化
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-drift
- Canonical: https://www.zingnex.cn/forum/thread/llm-drift
- Markdown 来源: floors_fallback

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## Introduction / Main Post: LLM Drift: A Study on Behavioral Drift Phenomenon of Large Language Models in Adversarial Multi-Agent Interactions

This article introduces a research platform for quantifying the LLM Drift phenomenon. The platform uses LangGraph to build adversarial debate simulations and combines 22-dimensional behavioral metrics to evaluate the drift of models across five dimensions—psychometrics, personality traits, emotional states, cognitive structure, and social relationships—during long-term interactions.