LN: Chen et al. (2025) — The Society of HiveMind: Multi-Agent Optimization of Foundation Model Swarms
Bibliographic Reference
Citation: Chen, Q., Wang, Z., Zhang, Y., Li, H., Liu, Z., & Sun, M. (2025). The society of HiveMind: Multi-agent optimization of foundation model swarms to unlock the potential of collective intelligence. arXiv:2503.05473. https://arxiv.org/abs/2503.05473 (This paper is in the PUMA project knowledge PDFs as “THE_SOCIETY_OF_HIVE_MIND_2503.05473v2.pdf” and “The_Society_of_HiveMind_Multi-Agent_Optimization_of_Foundation_Model_Swarms_2503.05473v2.pdf”)
Note: This is the correct arXiv:2503.05473 paper. DynTaskMAS uses arXiv:2503.07675 — these are two different papers with similar IDs.
Pass 1 — Bird’s Eye View (5 Cs)
| C | Assessment |
|---|---|
| Category | System proposal + framework |
| Context | Addresses how to collectively optimise a swarm of LLM agents through shared intelligence |
| Correctness | Empirical evaluation on task completion benchmarks. |
| Contributions | (1) Swarm-level optimisation of agent behaviours; (2) Collective intelligence emerges from agent interactions; (3) Better than individual agent optimisation |
| Clarity | Good. |
Relevance: ⭐⭐⭐
The swarm optimisation pattern is relevant for PUMA Stage 5 if multiple triage agents specialise through interaction with the same issue corpus. Background for the Smart PMO swarm architecture.