# AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction

arXiv: https://arxiv.org/abs/2506.11475 · HTML: https://mlsanity.com/paper/2506.11475/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Inequity aversion improves cooperation in intertemporal social dilemmas](https://mlsanity.com/paper/1803.08884/) (2018)
- [Learning with Latent Language](https://mlsanity.com/paper/1711.00482/) (2017)
- [A survey of the Vision Transformers and their CNN-Transformer based Variants](https://mlsanity.com/paper/2305.09880/) (2023)
- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [Malthusian Reinforcement Learning](https://mlsanity.com/paper/1812.07019/) (2018)
- [A Survey of the Self Supervised Learning Mechanisms for Vision Transformers](https://mlsanity.com/paper/2408.17059/) (2024)
- [A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis](https://mlsanity.com/paper/2503.11101/) (2025)

## Built on by (most cited first, 2 of 2)

- [Before Humans Join the Team: Diagnosing Coordination Failures in Healthcare Robot Team Simulation](https://mlsanity.com/paper/2508.04691/) (2025)
- [Multimodal Multi-Agent Ransomware Analysis Using AutoGen](https://mlsanity.com/paper/2601.20346/) (2026)

## Abstract (arXiv)

This paper introduces LUCID-MA (Learning and Understanding Crime through Dialogue of Multiple Agents), an innovative AI powered framework where multiple AI agents collaboratively analyze and understand crime data. Our system that consists of three core components: an analysis assistant that highlights spatiotemporal crime patterns; a feedback component that reviews and refines analytical results; and a prediction component that forecasts future crime trends. With a well-designed prompt and the LLaMA-2-13B-Chat-GPTQ model, it runs completely offline and allows the agents undergo self-improvement through 100 rounds of communication with less human interaction. A scoring function is incorporated to evaluate agent performance, providing visual plots to track learning progress. This work demonstrates the potential of AutoGen-style agents for autonomous, scalable, and iterative analysis in social science domains, maintaining data privacy through offline execution. It also showcases a computational model with emergent intelligence, where the system's global behavior emerges from the interactions of its agents. This emergent behavior manifests as enhanced individual agent performance, driven by collaborative dialogue between the LLM-based agents.