Taxonomies of World Models › Limitations and Impact

The taxonomic view has real limits, and pretending otherwise leads to poor research taste. The five-way state axis is a simplification of a continuum: production systems routinely blend families, running a latent core with a token interface and object-slot decoders, and the labels describe components more often than whole systems. The explicit/implicit distinction blurs further in agents that distill an explicit planner into an implicit policy, or distill an implicit value function back into an explicit model for analysis. Treat every placement in this chapter as a statement about the dominant interface, not a metaphysical commitment.

There is also a deeper worry: axes that describe architecture may not describe capability. Two models with identical taxonomic positions can differ enormously in what they know, and the same architecture can express radically different physics depending on data and training. This is why the handbook devotes Part XI and Part XII to evaluation and reliability rather than treating architecture as destiny. A taxonomy tells you what questions to ask of a model; only experiments tell you the answers. Concretely, knowing that a model is a stochastic latent model tells you to check its calibration and its rollout compounding, but it tells you nothing about whether its latents distinguish the situations your safety case cares about, and no amount of taxonomic reasoning substitutes for probing that directly.

The taxonomic framing had substantial practical value. It gave the field a shared vocabulary during a period of rapid architectural change, letting results transfer across communities that did not know they were working on the same problem: the Dreamer lineage's latent rollouts, MuZero's decision-scoped implicitness, video foundation models' pixel-scale priors, and neuro-symbolic systems' relational scaffolds are all legible as positions on the axes above, with their tradeoffs largely predictable from position alone. For practitioners, the axes function as a design checklist. Before building, you can now ask: what will query this model, in what format, over how long a horizon, under how much noise, and across how many tasks? Each question eliminates half the design space, and the chapters ahead assume you can answer them.