MultiWorldBench is a diagnostic Minecraft benchmark designed to test whether independently controlled views can describe a single shared world. It contains 495 case configurations across seven task suites and ten capabilities, such as independent control, cross-view motion, shared-state synchronization, persistence, structural reasoning, concurrent interaction, and delayed revisit. The benchmark evaluates systems like Solaris, Gamma-World, and MineWorld, revealing that none achieve high scores in state persistence, structural consistency, spatial reasoning, or building-identity preservation, and that Gamma-World and Solaris perform best in different subsets of capabilities.
arXiv:2609.24308v1 Announce Type: new Abstract: Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, int...
Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, interaction, and modification. We introduce HappyWor...
arXiv:2610.20240v1 Announce Type: new Abstract: A multiplayer world model must predict how the independent actions of several players change a shared environment, including changes outside a player's...
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.
MineExplorer is a benchmark designed to assess the open‑world exploration abilities of multimodal large language models (MLLMs) in Minecraft. It filters out tasks that rely heavily on Minecraft‑specific knowledge, organizes tasks into ReAct‑style capabilities, and composes atomic tasks into implicit multi‑hop challenges. A multi‑agent synthesis workflow creates reliable task graphs, sandbox scenes, and rule‑based milestone evaluators, and human evaluation confirms its superiority over a single‑agent baseline. Experiments show that while advanced MLLMs can handle many single‑hop tasks, they struggle with longer trajectories that require coordinating hidden prerequisites, and larger models or different thinking modes do not consistently improve performance.
arXiv:2607. 29218v1 Announce Type: new Abstract: With the prosperity of the large language models (LLMs), it has become an interesting topic: how do LLM-based agents work in Minecraft?
arXiv:2606. 31672v2 Announce Type: replace-cross Abstract: Despite rapid progress in interactive world models (IWMs), existing benchmarks evaluate action following only at trajectory level and ignore memory and interaction physics.
arXiv:2610.02205v1 Announce Type: new Abstract: Programmable world models separate executable dynamics from visual generation, offering a promising foundation for next-generation game engines. Howeve...
WorldBench is a benchmark and automated judge for evaluating large language models that generate interactive 3D worlds using Three.js. It combines code inspection with dynamic visual analysis, requiring the model’s code to produce observable features such as biomes, physics, and seasonal cycles. The study shows that relying on either code or visual output alone can misjudge a model’s performance, with a mutation test revealing that code-only judging overestimates success by a third.
arXiv:2606. 31672v1 Announce Type: cross Abstract: Despite rapid progress in interactive world models (IWMs), existing benchmarks evaluate action following only at trajectory level and ignore memory and interaction physics.
arXiv:2610.10201v1 Announce Type: cross Abstract: Joint spatial-geometric and analytic function reasoning requires translating a perceived spatial configuration into a symbolic function whose execute...
GlobeBench is a new benchmark for evaluating text-based world models, comprising 1,632 tasks across five domains and nineteen simulation skills. It tests whether models can predict an environment’s response to an action based on the full agent trajectory, requiring understanding of state changes, environment rules, and action effects on related and unrelated objects. The benchmark compares predictions to execution-derived references using a hybrid architecture of deterministic gates and a semantic judge, and finds that GPT‑6 Astra tops the 20 tested models with a 49.57% success rate, while accuracy drops sharply when actions affect many dependent objects, leaving 631 tasks unsolved by any model.