Dual-Frontier: When Can an Agent Trust Its World Model?
Read the original on arXiv AI →The paper introduces Dual-Frontier, a learning principle that determines when an agent should trust its world model for decision-making. It formalizes the failure-attribution problem as a counterfactual decomposition of return loss and shows that its components cannot be identified from passive interaction, even for finite-horizon planners. Dual-Frontier allows a model‑guided decision only when the predicted advantage exceeds a certified bound on decision‑relevant world‑model error; otherwise, the agent focuses on verifying the model. The authors provide theoretical guarantees, adaptive evidence reuse, and experimental validation on controlled and realistic benchmarks, demonstrating improved decision quality and reliability.
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