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

• URL: http://arxiv.org/abs/2506.11475v2
• Date: Sun, 20 Jul 2025 10:54:02 GMT
• Title: AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction
• Authors: Syeda Kisaa Fatima, Tehreem Zubair, Noman Ahmed, Asifullah Khan,
• Abstract summary: This paper introduces LUCID-MA, an innovative AI powered framework where multiple AI agents collaboratively analyze and understand crime data.<n>Our system that consists of three core components: an analysis assistant highlights crime patterns; a feedback component that reviews and refines analytical results; and a prediction component that forecasts future crime trends.<n>A scoring function is incorporated to evaluate agent performance, providing visual plots to track learning progress.
• Score: 0.3749861135832073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
  The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
  arXiv  Detail & Related papers  (2025-07-28T17:58:12Z)
• Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories [18.129031749321058]
  Large Language Model (LLM)-based agents are increasingly employed to automate complex software engineering tasks.<n>Despite their widespread adoption, the internal decision-making processes of these agents remain largely unexplored.<n>We present a large-scale empirical study of the thought-action-result trajectories of three state-of-the-art LLM-based agents.
  arXiv  Detail & Related papers  (2025-06-23T16:34:52Z)
• Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities [117.49715661395294]
  Data structurization can play a promising role by transforming intricate and disorganized data into well-structured forms.<n>This survey presents a first systematic review of how graphs can empower AI agents.
  arXiv  Detail & Related papers  (2025-06-22T12:59:12Z)
• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users.<n>This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• Multi-Agent Actor-Critic Generative AI for Query Resolution and Analysis [1.0124625066746598]
  We introduce MASQRAD, a transformative framework for query resolution based on the actor-critic model.<n> MASQRAD is excellent at translating imprecise or ambiguous user inquiries into precise and actionable requests.<n> MASQRAD functions as a sophisticated multi-agent system but "masquerades" to users as a single AI entity.
  arXiv  Detail & Related papers  (2025-02-17T04:03:15Z)
• Poly-Autoregressive Prediction for Modeling Interactions [42.51313085280179]
  We propose Poly-Autoregressive (PAR) modeling, which forecasts an ego agent's future behavior.<n>We show that PAR can be applied to three different problems: human action forecasting in social situations, trajectory prediction for autonomous vehicles, and object pose forecasting during hand-object interaction.
  arXiv  Detail & Related papers  (2025-02-12T18:59:43Z)
• Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks [0.0]
  Hallucinations remain a significant challenge in current Generative AI models.<n>This study investigates how orchestrating multiple Artificial Intelligent Agents can help mitigate such hallucinations.
  arXiv  Detail & Related papers  (2025-01-19T11:19:25Z)
• Using Generative AI and Multi-Agents to Provide Automatic Feedback [4.883570605293337]
  This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts. The research addresses a key gap in the field by exploring how multi-agent systems, called AutoFeedback, can improve the quality of GenAI-generated feedback.
  arXiv  Detail & Related papers  (2024-11-11T22:27:36Z)
• Agent S: An Open Agentic Framework that Uses Computers Like a Human [31.16046798529319]
  We present Agent S, an open agentic framework that enables autonomous interaction with computers through a Graphical User Interface (GUI) Agent S aims to address three key challenges in automating computer tasks: acquiring domain-specific knowledge, planning over long task horizons, and handling dynamic, non-uniform interfaces.
  arXiv  Detail & Related papers  (2024-10-10T17:43:51Z)
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [112.04307762405669]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine.<n>G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System [4.612237040042468]
  CityGPT employs three agents to accomplish thetemporal analysis of IoT data. We have agnentized the framework, facilitated by a large language model (LLM), to increase the data comprehensibility. Our evaluation results on real-world data with different time show that the CityGPT framework can guarantee robust performance in computing.
  arXiv  Detail & Related papers  (2024-05-23T15:27:18Z)
• CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation [61.68049335444254]
  Multimodal large language models (MLLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments. We propose a Comprehensive Cognitive LLM Agent, CoCo-Agent, with two novel approaches, comprehensive environment perception (CEP) and conditional action prediction (CAP) With our technical design, our agent achieves new state-of-the-art performance on AITW and META-GUI benchmarks, showing promising abilities in realistic scenarios.
  arXiv  Detail & Related papers  (2024-02-19T08:29:03Z)
• It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
  Attack Graph (AG) represents the best-suited solution to support cyber risk assessment for multi-step attacks on computer networks. Current solutions propose to address the generation problem from the algorithmic perspective and postulate the analysis only after the generation is complete. This paper rethinks the classic AG analysis through a novel workflow in which the analyst can query the system anytime.
  arXiv  Detail & Related papers  (2023-12-27T10:44:58Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network [65.11999700562869]
  We propose a novel collaborative prediction unit (CoPU), which aggregates predictions from multiple collaborative predictors according to a collaborative graph. Our methods outperform state-of-the-art works on the three tasks by 28.6%, 17.4% and 21.0% on average.
  arXiv  Detail & Related papers  (2021-07-02T08:20:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.