mlsanityData to 14 Oct 2026

Balancing Autonomy and Alignment: A Multi-Dimensional Taxonomy for Autonomous LLM-powered Multi-Agent Architectures

arXiv 2310.03659 ↗First posted 5 Oct 2023cs.AI cs.MA cs.SEcited by 25

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Large language models (LLMs) have revolutionized the field of artificial intelligence, endowing it with sophisticated language understanding and generation capabilities. However, when faced with more complex and interconnected tasks that demand a profound and iterative thought process, LLMs reveal their inherent limitations. Autonomous LLM-powered multi-agent systems represent a strategic response to these challenges. Such systems strive for autonomously tackling user-prompted goals by decomposing them into manageable tasks and orchestrating their execution and result synthesis through a collective of specialized intelligent agents. Equipped with LLM-powered reasoning capabilities, these agents harness the cognitive synergy of collaborating with their peers, enhanced by leveraging contextual resources such as tools and datasets. While these architectures hold promising potential in amplifying AI capabilities, striking the right balance between different levels of autonomy and alignment remains the crucial challenge for their effective operation. This paper proposes a comprehensive multi-dimensional taxonomy, engineered to analyze how autonomous LLM-powered multi-agent systems balance the dynamic interplay between autonomy and alignment across various aspects inherent to architectural viewpoints such as goal-driven task management, agent composition, multi-agent collaboration, and context interaction. It also includes a domain-ontology model specifying fundamental architectural concepts. Our taxonomy aims to empower researchers, engineers, and AI practitioners to systematically analyze the architectural dynamics and balancing strategies employed by these increasingly prevalent AI systems. The exploratory taxonomic classification of selected representative LLM-powered multi-agent systems illustrates its practical utility and reveals potential for future research and development.

Key facts. Cited by 25 papers in the map (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 44 of its 103 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

44 of 103 references.

Closest ancestors · 6

Same direction, newest first

  1. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep
  2. 2023A Survey on Large Language Model based Autonomous Agents22 Aug
  3. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug
  4. 2023Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration11 Jul
  5. 2023SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks27 May
  6. 2023Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models26 Apr

Other references · 38

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
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26 more
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  2. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 11; 2026: 6.

25 papers.

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  1. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 126
  2. 2024Determinants of LLM-assisted Decision-Makingcited by 56
  3. 2024AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agentscited by 37
  4. 2025Creativity in LLM-based Multi-Agent Systems: A Surveycited by 23
  5. 2024Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMscited by 22
  6. 2024KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Modelscited by 17
  7. 2024Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulationcited by 13
  8. 2026The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMscited by 10
  9. 2025Position: Towards a Responsible LLM-empowered Multi-Agent Systemscited by 8
  10. 2025Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignmentcited by 7
  11. 2024Language Models as Critical Thinking Tools: A Case Study of Philosopherscited by 6
  12. 2025CrowdSelect: Synthetic Instruction Data Selection with Multi-LLM Wisdomcited by 6
13 more
  1. 2025Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectivescited by 5
  2. 2026TacoMAS: Test-Time Co-Evolution of Topology and Capability in LLM-based Multi-Agent Systemscited by 5
  3. 2024Follow-Me AI: Energy-Efficient User Interaction with Smart Environmentscited by 4
  4. 2025AURA: An Agent Autonomy Risk Assessment Frameworkcited by 4
  5. 2023Exploring Large Language Models to Facilitate Variable Autonomy for Human-Robot Teamingcited by 3
  6. 2025Exploring Agentic Artificial Intelligence Systems: Towards a Typological Frameworkcited by 3
  7. 2026Agentic Coding Needs Proactivity, Not Just Autonomycited by 3
  8. 2025Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulationscited by 2
  9. 2026Multi-Agent LLMs Fail to Explore Each Othercited by 2
  10. 2025HERA: Hybrid Edge-cloud Resource Allocation for Cost-Efficient AI Agentscited by 1
  11. 2025AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulationscited by 1
  12. 2026Agentic Frameworks for Reasoning Tasks: An Empirical Studycited by 0
  13. 2026Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agentscited by 0

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  5. 2026The Path Ahead for Agentic AI: Challenges and Opportunities
  6. 2024Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects
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