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ThinkTank: A Framework for Generalizing Domain-Specific AI Agent Systems into Universal Collaborative Intelligence Platforms

arXiv 2506.02931 ↗First posted 3 Jun 2025cs.MA cs.AI cs.LGcited by 0

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Abstract (arXiv)

This paper presents ThinkTank, a comprehensive and scalable framework designed to transform specialized AI agent systems into versatile collaborative intelligence platforms capable of supporting complex problem-solving across diverse domains. ThinkTank systematically generalizes agent roles, meeting structures, and knowledge integration mechanisms by adapting proven scientific collaboration methodologies. Through role abstraction, generalization of meeting types for iterative collaboration, and the integration of Retrieval-Augmented Generation with advanced knowledge storage, the framework facilitates expertise creation and robust knowledge sharing. ThinkTank enables organizations to leverage collaborative AI for knowledge-intensive tasks while ensuring data privacy and security through local deployment, utilizing frameworks like Ollama with models such as Llama3.1. The ThinkTank framework is designed to deliver significant advantages in cost-effectiveness, data security, scalability, and competitive positioning compared to cloud-based alternatives, establishing it as a universal platform for AI-driven collaborative problem-solving. The ThinkTank code is available at https://github.com/taugroup/ThinkTank

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 17 references that are in the map. First posted on arXiv 3 Jun 2025.

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References · 14

  1. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
  2. 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMscited by 431
  3. 2025Accelerating scientific discovery with Co-Scientistcited by 401
  4. 2024LLM Multi-Agent Systems: Challenges and Open Problemscited by 120
  5. 2024SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoningcited by 94
  6. 2024Reducing hallucination in structured outputs via Retrieval-Augmented Generationcited by 93
  7. 2025AgentRxiv: Towards Collaborative Autonomous Researchcited by 72
  8. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generationcited by 67
  9. 2024MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generationcited by 44
  10. 2024Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applicationscited by 19
  11. 2025RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integrationcited by 9
  12. 2024A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentialitycited by 8
2 more
  1. 2024Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directionscited by 5
  2. 2024DuetRAG: Collaborative Retrieval-Augmented Generationcited by 1

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  1. 2025Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study
  2. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents
  3. 2026Collaborative Agent Reasoning Engineering (CARE): A Three-Party Design Methodology for Systematically Engineering AI Agents with Subject Matter Experts, Developers, and Helper Agents
  4. 2025Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
  5. 2024Unlocking the Wisdom of Large Language Models: An Introduction to The Path to Artificial General Intelligence
  6. 2024MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming
  7. 2025Position Paper: Towards Open Complex Human-AI Agents Collaboration Systems for Problem Solving and Knowledge Management
  8. 2026AgentSkiller: Scaling Generalist Agent Intelligence through Semantically Integrated Cross-Domain Data Synthesis