A revised arXiv paper presents the LAND model, which combines large language models with agent-based simulation to study how individual interactions on networks build into collective influence and coordinated behavior. The work builds on the GhostField architecture and positions LLM-driven agents as a way to model social dynamics more realistically than rule-based agents alone.
A new arXiv preprint proposes designing collective information engines that are organized by differentiation rather than by consensus. The authors construct a minimal example showing how distinct roles can emerge within a population of agents and drive collective behavior in informational active matter. The work sits at the intersection of active matter physics and multi-agent systems research.
A new preprint introduces a mathematical approach that represents populations of interacting adaptive agents as probability waves, with a generalized behavioral intelligence equation yielding eigenmodes that can be tested empirically. The authors aim to capture how collective behavior emerges as artificial general intelligence systems adapt and interact.