NeuroBelief: Observer-Centric World Modeling of Information Acquisition in Multisequence MRI

World models typically predict how a system changes after an action. In information acquisition, however, the underlying system may remain unchanged while the observer's task state changes as new evidence becomes available. This raises a different prediction problem: before acquiring a candidate observation, can we anticipate how it would update the current task state? We study this problem in multisequence brain MRI and introduce NeuroBelief. The currently observed sequences are encoded into a spatial Pathology Belief. Given this belief and the identity of a candidate sequence, NeuroBelief predicts the Pathology Belief that would be formed after acquisition, without access to the candidate image. Once the sequence is acquired, the same encoder constructs the corresponding target state, allowing the predicted transition to be evaluated directly. On BraTS 2023 and BraTS Africa, the predicted transitions depend on both the current patient state and the candidate sequence, and generalize to observation and acquisition combinations excluded from transition training. Using these predicted states for sequential acquisition, NeuroBelief reduces the number of acquired sequences by 48.4% internally and 44.7% externally relative to acquiring all four sequences, while mean Dice remains 0.68 and 1.41 percentage points below the corresponding four sequence references. These results show that world modeling can also be used to predict how new observations change an observer's task state, providing a directly testable basis for adaptive information acquisition. Source code is provided in the supplementary material.