🤖 AI Summary
This study formalizes world model research as the problem of designing latent states under task-sufficiency constraints, aiming to retain essential information while discarding redundancy. To this end, it introduces a functional taxonomy of latent states—categorized by roles such as prediction, control, and planning—and develops a seven-dimensional evaluation framework centered on task sufficiency. The framework encompasses diverse technical approaches, including predictive embeddings, recurrent belief states, causal structures, latent action interfaces, embodied planning interfaces, and memory substrates. Empirical results demonstrate that latent state designs aligned with specific task requirements substantially outperform generic modeling strategies that prioritize maximal information preservation, thereby revealing critical distinctions between predictive sufficiency and control sufficiency.
📝 Abstract
A world model matters to an agent only through the state it constructs. That state must preserve some information, discard other information, and support some future function: prediction, control, planning, memory, grounding, or counterfactual reasoning. This paper treats world-model research as latent state design under sufficiency constraints.
We propose a functional taxonomy that groups methods by what their latent state is for, rather than by architecture or application domain: predictive embedding, recurrent belief state, object/causal structure, latent action interface, grounded planning interface, and memory substrate. These roles expose distinctions that architecture-based groupings hide, including the gap between predictive sufficiency and control sufficiency, and the gap between passive video prediction and counterfactual action modeling.
The taxonomy supports an evaluation framework that judges a model by the sufficiency constraint its latent state was built to satisfy. We compare methods along seven axes: representation, prediction, planning, controllability, causal/counterfactual support, memory, and uncertainty. We use the resulting matrix as a diagnostic for what a latent state preserves, discards, and enables.
The conclusion that follows is that an actionable world model is the one whose state construction matches the task, not the one that preserves the most information.