SOURCE-LINKED INTELLIGENCE
Dual-Frontier: When Can an Agent Trust Its World Model?
Learned world models are becoming essential to general-purpose agents: by predicting action consequences, they support planning and decision-making while reducing reliance on costly trial and error. This reliance creates a fundamental ambiguity: when a world-model-guided decision fails, the trajectory alone may not reveal whether the agent's decision rule or the world model caused the loss. We formalize this failure-attribution problem as a counterfactual decomposition of return loss and prove that its components are not identifiable from passive interaction, even for finite-horizon planners.
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-22T12:05:43.000Z
First collected: 2026-09-23T04:11:12.117Z. This is not the publication date.