AI RESEARCH
EvoSpark: Endogenous Interactive Agent Societies for Unified Long-Horizon Narrative Evolution
arXiv CS.CL
•
ArXi:2604.12776v1 Announce Type: new Realizing endogenous narrative evolution in LLM-based multi-agent systems is hindered by the inherent stochasticity of generative emergence. In particular, long-horizon simulations suffer from social memory stacking, where conflicting relational states accumulate without resolution, and narrative-spatial dissonance, where spatial logic detaches from the evolving plot. To bridge this gap, we propose EvoSpark, a framework specifically designed to sustain logically coherent long-horizon narratives within Endogenous Interactive Agent Societies.