NegativeSpace-WM: Learning to Rule Out Impossible Futures for World-Model Planning

World models help agents choose actions by predicting their consequences, but generating physically impossible futures wastes computation. Rejecting these futures before generation requires identifying physical conflicts while preserving feasible alternatives. We propose NegativeSpace-WM, a framework that learns minimal impossibility certificates to guide this decision. Each certificate describes a physical conflict, a repair with no unnecessary edits, and the futures that the conflict rules out. A shared model trained on valid simulated events and their violating and repaired variants learns to recognize conflicts in videos and predict them from observations, actions, and brief descriptions of proposed futures. These predictions guide independent physical checks; generation is skipped only when the checks rule out every future allowed by the candidate request. Experiments show that minimal repairs improve balanced accuracy on unseen physical settings by 2.2 percentage points over equally long redundant repairs. Certificate guidance also helps the verifier reach definite conclusions for more candidates than unguided search at the same checking budget. Across four planning task families, NegativeSpace-WM uses 33.20% less total GPU time per episode than generating every candidate, with task success of 81.6% versus 82.5%.