Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates
Complexity and interpretability rarely coincide: systems rich enough for complex behaviours to emerge are usually too opaque to question, while transparent ones are too simple for anything complex to emerge. A single large language model (LLM) is a static artefact, hardly exhibiting any of the emergent properties we associate with life. This changes through interaction: populations of LLMs display emergent dynamics absent from isolated models. Furthermore, LLMs can be endowed with persistent memory, tools and shared skills, and the capacity to initiate actions unprompted, i.e., turning LLMs agentic. In this paper, we argue that such collectives of agents can serve as a computational subst...
| Authors | Elias Najarro, Ane Espeseth, Eleni Nisioti et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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