SwarmWorld: Stigmergic technological evolution in societies of language-model agents
Score
71该论文提出新颖的 SwarmWorld 环境，首次用实验展示语言模型代理中环境痕迹驱动的技术演化，信息增量显著。
实验设计严谨，包含独立搜索基线和多种行为分析，结论有充分数据支撑。
对多智能体系统设计和去中心化协作架构有直接启发，但尚处研究阶段，落地需时间。
来自最新 arXiv 预印本，紧跟多智能体与大模型前沿，时效性强。
arXiv 为权威预印本平台，但论文尚未经过同行评审，信源可信度中等偏上。
Base 78.5 × T2 Source weight ×0.9 = 71
Curation line 70Selected
Why selected
The paper introduces the novel SwarmWorld framework and uses systematic experiments to reveal the key role of environmental traces in the evolution of agent societies, providing solid evidence.
AI commentary
「This paper is worth attention because it brings the nature-inspired stigmergy mechanism into the world of large-language-model agents and backs it with reproducible experiments. For researchers working on multi-agent cooperation and artificial general intelligence, it highlights the potential of 'environment as medium' without relying on dialogue or central scheduling. Engineers may also adopt SwarmWorld's simulation style as a new testbed for technological evolution by agents.」
Quick overview
SwarmWorld places language-model agents in an interactive simulated environment where initially homogeneous agents self-organize into evolving technological societies without assigned roles, recipes, or centralized workflows. Agents explore a spatial world, process resources, test materials, construct persistent artifacts, and write executable controllers that are evaluated by a deterministic simulator under unseen disturbances after the agents are removed. The results show that shared societies develop broader and more resilient technological portfolios than a strong best-of-N isolated-search baseline, although isolated search remains competitive for the strongest artifact. Agents differentiate into exploration, construction, maintenance, and coordination behaviors as the world matures. Technologies accumulate through collaborative construction, executable inheritance, and persistent agent-artifact networks, with most reuse beginning through physical observation rather than communication. The study concludes that physical stigmergy alone can support capable societies, while interaction drives persistent technological ecologies rather than universally superior individual inventions.
- In SwarmWorld, homogeneous LLM agents self-organize without predefined roles and coordinate indirectly through environmental traces.
- Shared societies produce broader, more resilient technology portfolios than a strong best-of-N isolated-search baseline, though isolated search remains competitive for the strongest artifact.
- Agents differentiate into exploration, construction, maintenance, and coordination behaviors, with most technology reuse starting from physical observation rather than communication.
Full text
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