arXiv:2610.16905v1 Announce Type: new Abstract: Cascading failures develop through coupled power flow redistribution, protection actions, and topology changes, making remedial control dependent on how alternative interventions modify the subsequent trajectory. This paper develops an action-conditioned graph predictor (ACGP) for multi-step cascade prediction and closed-loop mitigation. A same-state counterfactual branching procedure replays identical post-contingency grid states under multiple legal actions while preserving the exogenous trajectory, providing direct supervision for action-dependent electrical states, line connectivity, automatic protection trips, and terminal risk. ACGP is embedded in a receding-horizon controller that evaluates five-step candidate futures, executes the selected intervention, and replans from the observed next state. The final dataset contains 2,860 counter factual roots and 22,880 action-conditioned branches. On the held-out test set, ACGP achieves a five-step line-loading MAE of 0.0369 and a redispatch pairwise ranking accuracy of 0.806; action ablations reduce the ranking accuracy to approximately chance level. In closed-loop Grid2Op experiments, terminal-failure and cascade rates decrease from 53.3% and 51.1% under do-nothing operation to 12.2% and 7.8%, respectively, close to the simulator-reference values of 11.1% and 6.7%. The results show that same-state counterfactual prediction can support effective action comparison and feedback-based cascading-failure mitigation.