Runze Fan

I-EDI: Robust Self-Evolution Agents via Verifiable Counterfactual Simulation

Current self-evolution methods mostly chase recall, they learn from any trajectory that ends with the right answer, even when the path relies on a "lucky guess". Such reasoning appears valid in-distribution but often fails the moment the task shifts slightly. We argue that robust evolution implies S