Knowing What to Change Is Not Enough: State-Transition Locality in Foundation Models

Foundation models increasingly update persistent structured state from new evidence. A reliable transition must change the variables supported by the evidence and preserve the rest. Across 205,920 controlled outputs from 13 open foundation models, these capabilities diverge: models with near-perfect target correctness can still have poor complement retention. Qwen3.8-27B, for example, reaches 99.5% joint target correctness but only 29.7% conditional retention. We formalize this failure as update spillover and separate support recovery, value prediction, and complement preservation. We then run a $2\times 2$ controlled interface intervention across ten models, varying whether transition support is predicted or provided and whether the model emits a full vector or a local update. Predicted sparse deltas underperform free full-vector decoding for nine of ten models. The oracle-local interface preserves the complement by construction and yields 86.5–100% exact transition accuracy. Explicit deterministic slot–value evidence nearly closes the gap. The hard regime is semantic support inference followed by scoped update execution, making transition locality a distinct reliability criterion for models that maintain persistent state.