Embodied robots need more than a description of the current image: they must estimate how the scene may change under motion, contact, and partial observation. Vision-centric world models provide this predictive layer, but they expose it through different state interfaces. This paper organizes the literature into four families according to the state available to planning: future observations, compact latent states, geometry-structured maps, and persistent entities or relations. The comparison examines how each state is formed, advanced, and queried; where inference and planning costs arise; and which errors matter in physical use. Representative methods show that observation prediction retains interpretable appearance but makes repeated rollout expensive. Latent dynamics reduce that cost while risking the loss of contact-scale variables. Geometric states support pose, clearance, and occupancy queries, although their reliability depends on calibration and timely updates. Entity-relational states preserve object identity and task relations, yet they remain vulnerable to binding errors under occlusion. Evaluation is therefore linked to the exposed state rather than to a single generic score. The resulting framework clarifies where temporal prediction, persistent geometry, and semantic identity complement one another in embodied planning.
Yi-Ning Li· Applied and Computational En...· 0 citations
As AI agents become participants in the development of their successors, they reshape both the production of intelligence and the role of human researchers. We introduce Atria Dawn Preview, a foundation agentic language model designed for scientific research and engineering workflows, with the goal of expanding the frontier of agent productivity in the real world. This model is trained via a Verifiable Experience Pipeline that connects tool-mediated interactions to executable environments and externally verified outcomes. Across 16 benchmarks spanning real-world research, engineering, and digital work, Atria Dawn Preview is competitive with frontier agents and achieves the highest reported score on five of them. Beyond standalone performance, we examine the real research-and-development process behind this model as a case study of human--AI collaboration, analyzing 769 task records from 56 participants together with agent logs. When asked to evaluate completed tasks under comparable conditions, participants rated about one-third of completed AI-assisted tasks as infeasible without AI. More strikingly, agents frequently propose methods and implement revisions, while humans retain most final decisions and guide exploration through judgment and feedback. These observations indicate a shift from task-level execution to project-level partnership, with human effort concentrating on what is worth pursuing and how evidence should guide research. Progress toward more autonomous AI research must therefore advance both the capacity for discovery and the capacity for meaningful human oversight, preserving accountable human authority over the risks and direction of continued development.
Hong-Lin Guo, Tao Gui, Yicheng Chen et al.· 0 citations
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