Low-Rank Dynamics-Effective Latent Carriers for Counterfactual Rollout in Learned World Models

We ask whether a compact, directly addressable hidden-state intervention can place a learned world model on an intended counterfactual future and then let the model’s own dynamics carry that future forward. In a controlled two-object collision environment, we study a 192-dimensional recurrent world model and construct candidate intervention carriers from training-only counterfactual-minus-factual hidden differences. An affine map predicts carrier coordinates from the factual state and requested edit without access to the native counterfactual hidden state at test time. For bounded Single velocity edits, rank 4 is the smallest tested rank satisfying the registered development criteria. A one-shot rank-4 patch launches a 12-transition autonomous rollout, and the frozen procedure satisfies a preregistered 2-of-3 fresh-checkpoint replication rule and remains reusable at nearby anchors. The same Single-derived carrier and Single-only map also support bounded same-object Joint requests. Across matched training regimes, broader counterfactual intervention support is associated with better Joint rollout fidelity and more additive hidden responses. Carrier-relative diagnostics show that the rank-4 subspace is not dynamically closed: its effect couples strongly to the wider recurrent state. These results support a compact intervention-entry interface rather than an intrinsic four-dimensional state, and suggest addressable latent interfaces as a potential design target for controllable world models.