AI RESEARCH
PROWL: Prioritized Regret-Driven Optimization for World Model Learning
arXiv CS.AI
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ArXi:2605.18803v1 Announce Type: cross Modern action-conditioned video world models achieve strong short-horizon visual realism, yet remain unreliable on rare, interaction-critical transitions that dominate downstream planning and policy performance. Because passive nstration data systematically under-samples these high-impact regimes, improving robustness requires actively eliciting model failures rather than relying on their natural occurrence. We