Adaptive Event-Driven Labeling: A Neuro-Symbolic Multiagent Framework for Causal Inference in Non-Stationary Time Series
This paper proposes Adaptive Event Driven Labeling (AEDL), a novel neurosymbolic framework that synthesizes unstructured semantic data with formal causal inference to disentangle simultaneous supply and demand shocks and introduces a Heterogeneous Multiagent Discussion (HAD) architecture.