# Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems

arXiv: https://arxiv.org/abs/2307.06187 · HTML: https://mlsanity.com/paper/2307.06187/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 12 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Large Language Models Can Self-Improve](https://mlsanity.com/paper/2210.11610/) (2022)
- [Extending MAPE-K to support Human-Machine Teaming](https://mlsanity.com/paper/2203.13036/) (2022)
- [Engineering Cooperative Smart Things based on Embodied Cognition](https://mlsanity.com/paper/1801.04345/) (2018)

## Built on by (most cited first, 30 of 31)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Empowering Biomedical Discovery with AI Agents](https://mlsanity.com/paper/2404.02831/) (2024)
- [AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents](https://mlsanity.com/paper/2408.08089/) (2024)
- [Exploring Autonomous Agents through the Lens of Large Language Models: A Review](https://mlsanity.com/paper/2404.04442/) (2024)
- [Multi-Agent Security Tax: Trading Off Security and Collaboration Capabilities in Multi-Agent Systems](https://mlsanity.com/paper/2502.19145/) (2025)
- [Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias](https://mlsanity.com/paper/2401.14589/) (2024)
- [Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents](https://mlsanity.com/paper/2311.13148/) (2023)
- [Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competencies](https://mlsanity.com/paper/2402.03628/) (2024)
- [YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2410.03997/) (2024)
- [Beyond Task Completion: An Assessment Framework for Evaluating Agentic AI Systems](https://mlsanity.com/paper/2512.12791/) (2025)
- [Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignment](https://mlsanity.com/paper/2510.14008/) (2025)
- [Formalizing the Safety, Security, and Functional Properties of Agentic AI Systems](https://mlsanity.com/paper/2510.14133/) (2025)
- [ORCH: many analyses, one merge-a deterministic multi-agent orchestrator for discrete-choice reasoning with EMA-guided routing](https://mlsanity.com/paper/2602.01797/) (2026)
- [A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor](https://mlsanity.com/paper/2405.16887/) (2024)
- [Integrated Design and Governance of Agentic AI Systems through Adaptive Information Modulation](https://mlsanity.com/paper/2409.10372/) (2024)
- [Investigating Tax Evasion Emergence Using Dual Large Language Model and Deep Reinforcement Learning Powered Agent-based Simulation](https://mlsanity.com/paper/2501.18177/) (2025)
- [LLM-based Multi-Agent System for Intelligent Refactoring of Haskell Code](https://mlsanity.com/paper/2506.19481/) (2025)
- [DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis](https://mlsanity.com/paper/2604.01236/) (2026)
- [AgentArcEval: An Architecture Evaluation Method for Foundation Model based Agents](https://mlsanity.com/paper/2510.21031/) (2025)
- [A Self-Healing Framework for Reliable LLM-Based Autonomous Agents](https://mlsanity.com/paper/2605.06737/) (2026)
- [RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA](https://mlsanity.com/paper/2510.20505/) (2025)
- [Integrating Machine Learning into Belief-Desire-Intention Agents: Current Advances and Open Challenges](https://mlsanity.com/paper/2510.20641/) (2025)
- [Neural Router: Semantic Content Matching for Agentic AI](https://mlsanity.com/paper/2605.25701/) (2026)
- [Business and ethical concerns in domestic Conversational Generative AI-empowered multi-robot systems](https://mlsanity.com/paper/2401.09473/) (2024)
- [Cognitive Insights and Stable Coalition Matching for Fostering Multi-Agent Cooperation](https://mlsanity.com/paper/2405.18044/) (2024)
- [Communicative Agents for Slideshow Storytelling Video Generation based on LLMs](https://mlsanity.com/paper/2509.01277/) (2025)
- [MELD: A Protocol for Merging Knowledge Across Distributed Agentic Memories](https://mlsanity.com/paper/2608.16357/) (2026)

## Abstract (arXiv)

In autonomic computing, self-adaptation has been proposed as a fundamental paradigm to manage the complexity of multiagent systems (MASs). This achieved by extending a system with support to monitor and adapt itself to achieve specific concerns of interest. Communication in these systems is key given that in scenarios involving agent interaction, it enhances cooperation and reduces coordination challenges by enabling direct, clear information exchange. However, improving the expressiveness of the interaction communication with MASs is not without challenges. In this sense, the interplay between self-adaptive systems and effective communication is crucial for future MAS advancements. In this paper, we propose the integration of large language models (LLMs) such as GPT-based technologies into multiagent systems. We anchor our methodology on the MAPE-K model, which is renowned for its robust support in monitoring, analyzing, planning, and executing system adaptations in response to dynamic environments. We also present a practical illustration of the proposed approach, in which we implement and assess a basic MAS-based application. The approach significantly advances the state-of-the-art of self-adaptive systems by proposing a new paradigm for MAS self-adaptation of autonomous systems based on LLM capabilities.