Lars Ø. Johansen

Conformal-AEDL: Uncertainty-Calibrated Neuro-Symbolic Multiagent Causal Inference for Non-Stationary Time Series

Conformal-AEDL is presented, a resource-efficient extension that couples heterogeneous residual and propagation agents with reliability-weighted consensus, an explicit symbolic compliance layer, and online conformal prediction sets and adapts the conformal error budget when score surprises indicate rapid drift.