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 ag
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownIdentifying Interactions at Scale for LLMs →
- LinkedLinked via unknownagent-tools →
- LinkedLinked via unknownIEEE Rolls Out Large Language Models Virtual Training Course →
- LinkedLinked via arxiv author · 85%Elias Najarro →
“Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates”
- LinkedLinked via arxiv author · 85%Ane Espeseth →
“Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates”
- LinkedLinked via arxiv author · 85%Eleni Nisioti →
“Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates”