arXiv Computer Vision
WorldStream: Streaming Interactive 3D Worlds with Hierarchical Planning and Persistent State
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arXiv AI
Oct 5
World Editing: Intervening on Executable Worlds at Increasing Depth
The paper introduces the concept of world editing—intervening on an existing executable world while preserving unchanged properties—and defines intervention depth as a measure of how strongly edits affect world entities, dynamics, and systems. It presents IGMWorld and IGMBench, a benchmark of 110 tasks across Minecraft and Terraria, evaluating edits through deterministic executability, behavioral, preservation, and visual checks. Frontier coding agents achieve up to 78.2% task-level success and 94.8% criterion-level success, though reliability drops with deeper interventions and visual consistency remains low.
arXiv AI
Sep 10
Hi-FLoop: Hierarchical State-Feedback Loops for Multi-Timescale World Modeling
arXiv:2609.08796v2 Announce Type: cross Abstract: Multi-agent traffic simulation seeks diverse, coordinated, and physically realistic futures from maps and observed history. Long-horizon closed-loop...
arXiv AI
Jun 6
WorldFly: A World-Model-Based Vision-Language-Action Model for UAV Navigation
arXiv:2606. 06147v1 Announce Type: new Abstract: End-to-end Vision-Language-Action (VLA) models have shown promise in UAV navigation.
arXiv AI
Oct 31
AnyGoal: On World Model Generalization Beyond Expert Rollouts
The paper introduces AnyGoal, a benchmark that tests world models on goal‑shifted task variants where state and action factors are recombined beyond the combinations seen in expert rollouts. It evaluates how well different world‑model families generalize to these unseen recombinations across multiple environments, revealing consistent failures. The study analyzes dynamics and representation shortcomings to identify where current models succeed and where they struggle in goal‑shifted prediction and planning.
arXiv AI
Aug 7
WorldClaw: Agentic 3D Open-World Generation at Scale
arXiv:2608. 05248v1 Announce Type: new Abstract: Generating large-scale, freely explorable 3D worlds from open-ended text remains challenging because a system must jointly maintain global spatial coherence, rich local content, and explicit assets suitable for downstream editing and reuse.
arXiv Machine Learning
Oct 15
StateCraft: Augmenting Video World Models with Abstract-State Reasoning
StateCraft introduces a new architecture that separates a persistent, updatable abstract state from the visual state in video world models. By pairing a language model with a video diffusion model, it tracks entity changes over time and conditions visual generation on this abstract state. The approach is evaluated on generative interfaces and a new benchmark, showing improved generation quality, memory usage, and controllability, while enabling direct editing of unseen states and extending to spatial 3D environments.