Controllable world simulation requires more than visually plausible frames: scenes must retain persistent state, coordinated actions, and editable temporal structure. Generative world models offer flexible visual synthesis, but their outputs do not always expose the objects and transitions needed for precise revision. Graphics engines provide executable state and direct control, yet translating language into valid operations requires specialized knowledge of assets, geometry, and interdependent APIs. We present CineU (Cinematize from Unreal), an agentic system for language-driven world orchestration and cinematic choreography within Unreal Engine. From a code-based world modeling perspective, an Unreal project serves as a persistent, inspectable world representation, while agent-generated code specifies operations whose effects can be executed and verified. CineU combines hierarchical orchestration, cascaded asset retrieval and geometry-constrained placement, capability-aligned code synthesis, and multi-stage verification with localized repair. Shared scene and timeline context coordinates entities, actions, camera trajectories, and shot boundaries while supporting dependency-aware revisions. Experiments on scene construction, selective editing, multi-shot cinematography, and component ablations show how specialized execution knowledge and engine feedback improve task completion and targeted recovery. CineU illustrates how executable world state can complement learned visual generation to support controllable and revisable simulated worlds.
Prices formed by Web users on Polymarket, a blockchain-based prediction market, are now cited in the news and in a U.S. Army intelligence bulletin as early warnings of geopolitical events. However, it remains unclear whether these prices can serve as such warnings. This cannot be judged from accuracy, because most markets resolve No and accuracy can remain high even when many of the events that did happen are missed. In this paper, we study this question from two aspects, using all 1.24 billion on-chain trades in 2.37 million Polymarket markets through September 2026. First, from 3,686 markets on geopolitical conflicts, we derive 208 unscheduled events that did happen and measure how far in advance prices move before the first Web report of each event. Second, we separate the information in prices about whether an event happens from the information about when it happens. To do so, we group 1,551 contracts into 338 deadline ladders and read each ladder as one forecast of the event date. We then split its log score, and the profit of 9.3 million trades by 491,000 accounts, exactly into these two parts. We find that one day before they were reported, prices correctly predicted only 56% of the geopolitical events but all 51 scheduled central-bank decisions. Separating the two parts shows that only about one eighth of the information in prices concerns timing. A small group of large accounts earns timing profits, but beyond current prices, no group of accounts helps predict an event within days. A high price is therefore a sign that an event is likely rather than imminent. Near-term warnings read from the whole ladder are more often right than those read from a single contract. Platforms should report recall alongside accuracy, and show near-term probabilities read from the whole ladder.