arXiv:2608. 13456v1 Announce Type: new Abstract: World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution.
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World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In this paper, we study WMs from a causal perspective across multiple levels of abstraction, ranging from perceptual observations to building a conceptual representation of the structure governing the environment dynamics.
arXiv:2608. 07214v1 Announce Type: cross Abstract: Modern AI is no longer a single model but an ecosystem: classical ML predictors, deep and multimodal models, large language models, and agents, each trained and tuned over different data sources and each producing outputs at scale that become inputs to the others.
The paper introduces Representational Empowerment (RepEmp), a metric for selecting representational elements that enhance an agent’s future modeling and planning abilities. Using a hierarchical Curator-Actor architecture, RepEmp is tested in three experiments: a closed‑vocabulary causal‑learning task, simulations showing improved structure recovery and transfer, and an open‑vocabulary planning domain where an LLM‑augmented Curator builds compact symbolic libraries. Ablation studies confirm that RepEmp is essential for these gains, positioning it as a key principle for continual model construction under resource constraints.
The paper introduces Representational Empowerment (RepEmp), a metric for selecting representational elements that enhance an agent’s future modeling and planning abilities. It applies RepEmp within a hierarchical Curator-Actor framework and demonstrates its effectiveness across three experiments: a closed‑vocabulary causal‑learning task, simulations showing improved structure recovery and transfer, and an open‑vocabulary planning domain where an LLM‑augmented Curator builds compact symbolic libraries that generalize better than baselines. Ablation of RepEmp removes these benefits, underscoring its role in guiding continual model construction under resource constraints.
arXiv:2609.26037v1 Announce Type: new Abstract: Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for lear...
arXiv:2609.36985v1 Announce Type: cross Abstract: The central challenge of world modeling is to learn representations that capture how the world evolves. However, existing world models predominantly...