Tag
AlayaVista is a camera-controllable streaming video world model that decouples panoramic scene evolution from perspective video synthesis, supported by the large-scale MUGEN dataset for high-fidelity interactive world modeling.
WMLLM is a self-evolving optimization-agent framework that uses predict-then-act world modeling with large language models to improve sample efficiency in black-box optimization, achieving state-of-the-art results in multi-objective molecular optimization.
Fable 5.1 is an AI agent-driven tool that generates browser-native 3D reconstructions of real-world places from open data, demonstrated with a detailed model of Union Square in San Francisco.
ReWorld presents an interactive world model that decouples short-horizon control from long-horizon memory using mixed attention windows and distribution-matching LoRA distillation, enabling real-time video generation with high action fidelity and recall.
This paper proposes an agent-centric paradigm for world modeling, introducing 'Agent-Centric Interactive World Proxies' and organizing them into six functional forms across three progressive levels of agent improvement, offering a roadmap for building world proxies that empower agents to plan, learn, and evolve.
The paper introduces Mental World Modeling (MWM), a framework that integrates hidden mental states as core components of world models, and presents MENTIS, a training-free baseline. Experiments with 8 LLM-based world models show explicit mental-state modeling is essential for predicting human decisions in situated scenarios.
WorldDiT is a unified architecture for robotics world modeling and control, achieving the best performance on the LIBERO benchmark among methods that do not rely on a VLM for action generation, and lies on the reported Pareto frontier.
WorldDiT is a unified diffusion transformer architecture that couples action generation with visual world modeling, achieving strong performance on LIBERO simulation suites without relying on large pretrained vision-language models.
Explains SIGReg, a novel regularizer for JEPA that prevents representation collapse by forcing embeddings to follow an isotropic Gaussian distribution, with theoretical guarantees and a clean training loop.
Introduces Masked Visual Actions, a pixel-space control interface that expresses actions as partially revealed trajectories, enabling a single model to act as forward dynamics model, recover robot behavior, and support model-based planning and inverse modeling with only 15 hours of training data.
AlayaWorld is a 15B-parameter interactive video world model that generates 24-fps video at 540p and 720p, using autoregressive latent chunk generation with bounded visual context and distillation to reduce inference steps. It achieves state-of-the-art performance on the iWorld-Bench for long-horizon generation.
This paper introduces LingBot-World 2.0, an advanced world modeling system with unbounded interaction horizons, real-time 720p 60fps video generation, diverse interactive elements (e.g., attacking, spell-casting), and novel multi-agent behavior control via pilot and director agents, along with a shared multi-player interface.
OPINE-World introduces an LLM agent that learns an object-centric programmatic world model online through interaction, using ontology-error-prioritized exploration and cooperating hypothesis-test agents, achieving strong results on ARC-AGI-3.
This paper proposes the AGI Maze, a benchmark framework designed to evaluate the world-modeling capabilities of AI agents.
This paper introduces A2World, a diffusion-based world model pretrained on large-scale robot manipulation data to learn transferable dynamics priors. The model can be adapted into a real-world simulator (A2World-sim) for policy evaluation or a video-action prediction model (A2World-policy) for action prediction, demonstrating benefits for both simulator-centric and policy-centric robot learning.
This paper introduces Agent-Authored World Modeling (AAWM), a training procedure that constructs world-model supervision based on the policy's own decision needs rather than next-observation prediction, aligning the learning objective with the dynamics required for effective decision-making.
MemoBench is a diagnostic benchmark for evaluating video generation models' memory consistency in dynamically changing environments, where objects disappear and reappear in updated states. It includes 360 ground-truth clips and an evaluation suite combining automated metrics with VQA-based assessment, revealing insights into memory consistency challenges.
This paper introduces In-Context World Modeling (ICWM), a framework that enables robot policies to infer system variables from self-generated interactions, allowing adaptation to novel configurations without parameter updates by treating system identification as an in-context adaptation problem. It outperforms standard VLA baselines on novel camera viewpoints in simulation and real-world experiments.
Anthropic demonstrates that AI systems can now perform world-modeling, as evidenced by the Fable standoff experiment.
Discussion of recent agentic RL papers, highlighting action masking as a common technique and its evolution with world modeling papers like ECHO and PaW.