The paper introduces Code World Model, a framework that decouples world evolution from visual rendering by using a coding agent as a world brain. The agent reasons about events, generates executable code to maintain persistent state, and a proxy representation links this state to a video model for high‑fidelity visual output. Experiments with MiniMax‑H3 show that the system can follow proxy‑based spatiotemporal specifications while preserving rich visual dynamics, illustrating a new approach to open‑ended world modeling.
arXiv:2609.10540v1 Announce Type: new Abstract: Recent video world models generate increasingly realistic and interactive visual experiences, yet lack reliable mechanisms for maintaining persistent w...
The paper introduces CoDeR, a new paradigm for world modeling that explicitly builds an executable world using code rather than relying solely on visual observations. CoDeR translates high‑level concepts into structured world rules, executable dynamics, and perceptual observations through five complementary roles, enabling long‑term memory, open‑ended interactions, autonomous world evolution, and persistent multi‑agent dynamics. Experiments show that this framework extends the capabilities of existing world models and achieves state‑of‑the‑art performance across multiple evaluation settings.
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.
AgentGarten is a framework that links simulators and game engines with a shared neural renderer to create real‑time interactive virtual worlds. It uses a pretrained video model adapted to geometry conditions, refined with Adversarial Forcing, to produce realistic visual observations while maintaining consistent state and rules. The system enables agents to learn efficiently—achieving comparable performance in only four training rounds versus millions in traditional reinforcement learning—by distilling experience into playbooks that successive agents can inherit and improve upon.
Oneira is an interactive video world model that links open-ended generation with persistent interaction. It uses a coding agent to manage an extensible world state, allowing newly generated or discovered entities to become actionable and ensuring that changes made by an agent are retained across video segments. Experiments demonstrate that Oneira supports consistent interaction with new objects while preserving prior interaction effects over long horizons.
arXiv:2609.25652v1 Announce Type: new Abstract: Recent game world models support realistic visual simulation and interactive gameplay based on player inputs. However, they typically learn environment...
arXiv:2607. 27380v2 Announce Type: replace-cross Abstract: Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal evolution of a scene must be inferred implicitly from a highly compressed text prompt.
CoWorld Reasoner is an agentic framework that leverages a Video World Model (VWM) as a unified visual reasoning engine for Vision‑Language Models (VLMs). It exposes multiple visual operations through tools and skills, allowing the VLM to decide when to imagine, select an operation, generate visual content, and interpret the resulting evidence. The framework includes a VLM‑VWM Harness Co‑Evolution mechanism that jointly refines tools, skills, and execution code based on feedback, while keeping model weights fixed. Across seven benchmarks covering spatial, temporal, causal, and perceptual reasoning, CoWorld Reasoner outperforms direct VLM reasoning and improves accuracy on all benchmarks, achieving notable gains on MMSI and ERQA.
arXiv:2511.09057v4 Announce Type: replace-cross Abstract: A world model is a cognitive simulator of the real-world environment allowing biological agents to reason about how the world evolves, whethe...
arXiv:2608.27549v1 Announce Type: new Abstract: Physical understanding and reasoning depend on forming compact and generalizable representations of the world. While modern vision-language models can...
arXiv:2608. 08600v1 Announce Type: cross Abstract: World models have recently achieved impressive progress in visual prediction and interactive generation, but extending them to multi-agent environments introduces a fundamental scalability challenge.
WorldMind is a decoupled framework for state-aware NPC behavior in game world models, separating interactive world modeling into four layers: Understanding, Decision, Control, and Generation. It constructs a compact state from generated frames, reasons over it to plan NPC actions, translates actions into temporally aligned conditions, and synthesizes visual outcomes. Experiments on the newly introduced BOSS-140K dataset show that WorldMind achieves more tactically appropriate and coherent NPC behavior than baseline models in about 70% of pairwise comparisons.