Transferability of findings beyond Camera Dropbox
Determine the extent to which results obtained in the Camera Dropbox grid-world generalize to environments with richer state spaces, partial observability, continuous action spaces, or multi-agent dynamics.
References
Although the benchmark contains 25 tasks with substantial variation in scale, workforce, and dependency structure, the extent to which the same organizational principles may transfer to other embodied domains remains to be established.
The extent to which findings transfer to environments with richer state spaces, partial observability, continuous actions, or multi-agent dynamics is unknown.
What additional capacities are needed for world modeling in more complex settings is a question for future research.
Generalization to robots, partial observability, larger backbones, other planners, and learned rewards, goal costs, or positive-semidefinite latent metrics remains open.
The analysis presented in this paper is intentionally restricted to structured decision geometries satisfying the regularity assumptions introduced in Section~\ref{sec:structural_geometry}. Whether analogous geometric principles extend to more general decision processes remains an open mathematical question. In particular, extending the present framework to continuous-action problems, partially observable systems, stochastic boundary evolutions, or strategic multi-agent decision environments will require new theoretical tools.
Transfer to other world sizes, operation distributions, or environments with uncertain observations remains an empirical question.
We believe Intern-Decision can serve as a practical component of larger agent systems, but its generalization to arbitrarily large schemas and complex environments remains an open problem.