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arXiv AI By Yang Liu, Yuming Chen

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

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The paper investigates whether a small, directly addressable change in the hidden state of a learned world model can steer its future predictions along a desired counterfactual trajectory. Using a 192‑dimensional recurrent model in a two‑object collision setting, the authors identify low‑rank latent carriers—specifically a rank‑4 patch—that, when applied, successfully redirect a 12‑step autonomous rollout without further intervention. The study demonstrates that this compact intervention interface consistently works across independently trained checkpoints and intervention times, while various control experiments confirm the specificity of the effect.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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