Mind-Studio: Executable World Models with Lookahead Evaluation for Partially Observable Games
On the map: World models → Video-generation world models and simulators
Abstract (arXiv)
World-model synthesis aims to turn interaction experience into an internal model of environment dynamics. Existing symbolic approaches often fit observed transitions or mixtures of local rules, but they do not produce a complete executable program that can run independently of the real environment. We present Mind-Studio, a framework that synthesizes executable pygame-style world models from state-action-next-state trajectories using large language models. Mind-Studio combines entropy-selected traces with a lightweight game skill file containing object, action, and static scene information extracted from screenshots. We evaluate synthesis quality with a K-step lookahead fidelity protocol that compares generated world-model rollouts against Real-ALE rollouts from the same state. On Montezuma's Revenge, Mind-Studio improves chosen-action next-state prediction from 0.3% for PoE-World to 48.7% while verifying 5 of 8 subgoals; across Alien, Assault, and Skiing, it achieves stronger branch-level fidelity than prior learned lookahead sources.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Video-generation world models and simulators, one of 11 directions in World models. Builds on 5 of its 26 references that are in the map. First posted on arXiv 14 Jun 2026.
Builds on
5 of 26 references.
References · 5
- 2018World Modelscited by 1,513
- 2019Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoningcited by 151
- 2020PDDLGym: Gym Environments from PDDL Problemscited by 45
- 2023OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environmentscited by 30
- 2026Joint Learning of Hierarchical Neural Options and Abstract World Modelcited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026WorldMind: Decoupled Game World Model for State-Aware NPC Behavior
- 2026GameWAM: A World Action Model for Video Games
- 2026PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives
- 2026PatchWorld: Gradient-Free Optimization of Executable World Models for Agent Environments
- 2026StatePlay: State-Aware Game World Models for Mechanics-Consistent Generation
- 2026PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models
- 2026Programmable World Model
- 2026WildWorld: A Large-Scale Dataset for Dynamic World Modeling with Actions and Explicit State toward Generative ARPG