arXiv:2605. 29930v2 Announce Type: replace Abstract: Modern societies possess more information than ever before, yet they do not converge toward a single shared understanding.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, enabling agents to predict, plan, and reason within learned representations.
arXiv:2511. 22226v2 Announce Type: replace Abstract: The standard theory of model-free reinforcement learning assumes that the environment dynamics are stationary and that agents are decoupled from their environment, such that policies are treated as being separate from the world they inhabit.
arXiv:2608. 10327v1 Announce Type: new Abstract: Can AI systems be aligned to human values?
arXiv:2507. 05169v4 Announce Type: replace-cross Abstract: World Model, the algorithmic simulator of the real-world environment which biological agents experience and act upon, has been an emerging topic in recent years due to the rising need to develop virtual agents with artificial (general) intelligence.
Large Language Models (LLMs) are frequently portrayed as general-purpose solvers capable of solving arbitrary tasks. We argue that this view overlooks a fundamental constraint: language is a compressed and capacity-limited interface for conveying task information.
arXiv:2607. 11270v1 Announce Type: cross Abstract: Learning, at its core, extends beyond memorization to the ability to reason and solve novel problems by navigating a space of possibilities.