How can LLMs improve human-AI teamwork?
Position Paper: Towards Open Complex Human-AI Agents Collaboration System for Problem-Solving and Knowledge Management
May 2, 2025
https://arxiv.org/pdf/2505.00018This paper surveys the field of human-AI agent collaboration, particularly for complex problem-solving and knowledge management. It proposes a new conceptual framework, the Hierarchical Exploration-Exploitation Net (HE2-Net), to unify existing research and guide future development.
Key points for LLM-based multi-agent systems include:
- LLMs as core components: LLMs can serve as reasoners, planners, and communicators within agents. Challenges include limitations in coherence, consistency, explainability, and efficiency, especially in long contexts. Techniques like chain-of-thought prompting, tree-of-thoughts reasoning, self-consistency aggregation, and game-theoretic equilibrium selection are explored to address these challenges.
- Tool integration and coordination: LLMs can interact with external tools to enhance capabilities. Key aspects include structured tool invocation, retriever-aware fine-tuning, and standardized communication protocols. Challenges remain in feedback incorporation and dynamic planning.
- Multi-agent coordination: Frameworks like AutoGen, AGENTVERSE, and MetaGPT exemplify various approaches to coordinating LLM-powered agents. Key considerations include goal alignment, task decomposition, dynamic role assignment, and communication protocols.
- Cybernetic perspectives: Cybernetic principles, such as feedback loops and adaptive agency, are crucial for building robust multi-agent systems. Methods incorporating internal and external criticism, structured memory, and environmental interaction are highlighted.
- Human-agent collaboration (HAAC): Humans play a vital role in defining goals, providing oversight, resolving conflicts, and adapting to unforeseen situations. Challenges include designing intuitive interfaces, addressing trust deficits and sycophancy, and balancing agent autonomy with human control.
- Collaborative epistemology: Knowledge management is crucial for HAAC. Constructivist learning, conversation theory, and entailment meshes are presented as frameworks for building shared understanding and knowledge.
- Meta-synthesis: This framework, originally developed for integrating human expertise, offers valuable insights for incorporating AI agents into complex problem-solving processes. Key adaptations include refining models through iterative consensus, addressing data-belief asymmetries, and handling evolving boundaries between agents and their environment. Petri Nets are proposed as a formalism for modeling and analyzing such systems. The HE2-net, inspired by these ideas, aims to formally represent multi-agent systems involving LLMs.