Filtered to world models · clear filter
This paper proposes a method to scale automatic research agents by replacing environment execution with a world model, which can reduce training costs and improve performance. Practitioners might care about this approach because it can accelerate training times and lead to better results for complex AI tasks.
This paper investigates whether video generation models, which are a type of world model, have a basic understanding of object permanence and can be improved through training. Practitioners interested in building human-like physical intelligence may care about this research as it could lead to more realistic and interactive AI systems.
This paper proposes a way to improve the efficiency of training world models by using game development as a source of reward signals and trajectory data, allowing for more effective post-training of large language models using reinforcement learning. Practitioners might care about this approach because it could lead to more scalable and effective world models for applications like dialogue systems and visual question answering.
This paper develops a world model that allows embodied agents to predict action consequences for both locomotion and manipulation across different robot and human embodiments. Practitioners might care about this research because it could lead to more sophisticated robots that can interact with their environment in a more human-like way.
This paper evaluates whether a multimodal generative model can reason about the physical world by testing its ability to integrate information from different modalities, such as text, images, video, and audio. Practitioners might care about this research because it can help develop more advanced models that can better understand and generate complex scenarios.
This paper develops a new AI model called EVOKE that can generate interactive worlds without running out of memory or taking too long. Practitioners might care about EVOKE because it can support long-term generation and adaptation in complex environments.
This paper develops a new method for evaluating world models, called HarnessEval-W, which provides more detailed and justifiable results than existing benchmarks. Practitioners might care about HarnessEval-W because it can help them build more trustworthy world models that better align with human preferences.
This paper improves autoregressive video generation by predicting future chunks of a video without relying on the intervening visual context, allowing for more accurate modeling of scene evolution. Practitioners might care because this approach can lead to more physically plausible videos while reducing computational costs.
This paper develops a new framework for world modeling that takes into account the mental state of agents, which is essential for predicting human decisions. Practitioners caring about human decision-making and planning might find this research useful.