Dual-Frontier: When Can an Agent Trust Its World Model?

TL;DR - Dual-Frontier is a framework for deciding when an agent can safely act on predictions from a learned world model. It addresses the otherwise unidentifiable question of whether failures stem from the agent’s decision rule or inaccurate model predictions.

• Proves that decision error and world-model error cannot be disentangled from passive interaction trajectories, even with finite-horizon planning.
• Admits a model-guided decision only when its predicted advantage exceeds a certified bound on decision-relevant model error; otherwise, it prioritizes model verification.
• Provides action-conditioned value bounds and a closed-loop extension guaranteeing non-decreasing return for admitted decisions.
• Experiments with learned models and cross-backbone tool-use benchmarks report improved decision quality and reliability from this verify-then-promote approach.