# CAMF: Collaborative Adversarial Multi-agent Framework for Machine Generated Text Detection

arXiv: https://arxiv.org/abs/2508.11933 · HTML: https://mlsanity.com/paper/2508.11933/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 16 Aug 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 15 of 15)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://mlsanity.com/paper/2305.15047/) (2023)
- [Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising](https://mlsanity.com/paper/2508.05352/) (2025)
- [Ai.llude: Encouraging Rewriting AI-Generated Text to Support Creative Expression](https://mlsanity.com/paper/2405.17843/) (2024)
- [Coherency Improved Explainable Recommendation via Large Language Model](https://mlsanity.com/paper/2504.05315/) (2025)
- [Graph Neural Network Enhanced Sequential Recommendation Method for Cross-Platform Ad Campaign](https://mlsanity.com/paper/2507.08959/) (2025)
- [A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy](https://mlsanity.com/paper/2507.12098/) (2025)

## Abstract (arXiv)

Detecting machine-generated text (MGT) from contemporary Large Language Models (LLMs) is increasingly crucial amid risks like disinformation and threats to academic integrity. Existing zero-shot detection paradigms, despite their practicality, often exhibit significant deficiencies. Key challenges include: (1) superficial analyses focused on limited textual attributes, and (2) a lack of investigation into consistency across linguistic dimensions such as style, semantics, and logic. To address these challenges, we introduce the Collaborative Adversarial Multi-agent Framework (CAMF), a novel architecture using multiple LLM-based agents. CAMF employs specialized agents in a synergistic three-phase process: Multi-dimensional Linguistic Feature Extraction, Adversarial Consistency Probing, and Synthesized Judgment Aggregation. This structured collaborative-adversarial process enables a deep analysis of subtle, cross-dimensional textual incongruities indicative of non-human origin. Empirical evaluations demonstrate CAMF's significant superiority over state-of-the-art zero-shot MGT detection techniques.