LN: Talebirad & Nadiri (2023) — Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents
Bibliographic Reference
Citation: Talebirad, Y., & Nadiri, A. (2023). Multi-agent collaboration: Harnessing the power of intelligent LLM agents. arXiv:2306.03314. https://arxiv.org/abs/2306.03314
Important Note
Overview
The bibliography entry lists arXiv:2312.04677 (a mathematics paper) as the ID. The correct arXiv ID is 2306.03314, verified on Semantic Scholar and arXiv.
Pass 1 — Bird’s Eye View (5 Cs)
| C | Assessment |
|---|---|
| Category | Survey / position paper |
| Context | Early survey of LLM-based multi-agent systems; precedes MetaGPT and AutoGen |
| Correctness | Conceptual survey, not empirical. References real systems. |
| Contributions | (1) Taxonomy of multi-agent LLM architectures; (2) Communication patterns (hierarchical, peer-to-peer, broadcast); (3) Task decomposition strategies for complex problems |
| Clarity | Good. Accessible overview. |
Relevance: ⭐⭐⭐⭐
Provides the theoretical taxonomy for classifying PUMA’s multi-agent architecture choices (Stage 4–5).
PUMA Connection
The taxonomy in this survey helps justify PUMA’s architectural choice for Stage 5: hierarchical (Manager Agent orchestrates specialist agents) vs. peer-to-peer (agents negotiate task ownership). Reference for Ch.2 architecture section.