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Hi-FLoop: Hierarchical State-Feedback Loops for Multi-Timescale World Modeling

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Multi-agent traffic simulation seeks diverse, coordinated, and physically realistic futures from maps and observed history. Long-horizon closed-loop generation must reconcile multiple decision time scales while its context evolves with generated states. Existing methods often unfold long futures from an initial scene and resolve intent, interaction, and motion monolithically, weakening cross-scale consistency and adaptation. Multimodal rollout poses a further consistency problem: independently reselecting modes across agents or commits can stitch together incompatible futures instead of preser

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