EvoCast: Reliable Autonomous Research Agents for Iterative Forecasting Architecture Evolution

Excerpt

arXiv:2610.04517v1 Announce Type: new Abstract: Deep time-series forecasting models have rapidly diversified, yet adapting them to a specific task still requires extensive expert effort in model selection, mechanism diagnosis, architecture design, implementation, and evaluation. Existing AutoML methods are constrained by predefined search spaces, while general-purpose LLM research agents lack reliable control over experimental protocols and model promotion. We introduce EvoCast, a fully autonomo