Context Mismatch in LLMs: Stale Interaction Policies Across Task Boundaries

As interactions with large language models unfold across multiple turns, a conversation establishes not only facts but also an interaction policy about who should propose, verify, constrain, and decide. Prior work has examined how information and user preferences persist across turns, but has largely overlooked whether such policies expire when the conversation moves to a new task. We show that they can remain behaviorally active. We call this failure context mismatch: a model reuses a prior task's governance state even when the new task requires a different allocation of decision rights. Our account of boundary-conditioned state reuse predicts that errors arise when the carried state persists, the new task reads it differently, and the resulting shift crosses the current decision margin. Using paired evaluations across six reasoning benchmarks that hold current evidence and answer options fixed, we find a task-dependent crossover: obedience histories impair independent verification but help authorized delegation, with failures concentrated near the decision boundary. Within each of two architectures, bidirectional state exchanges transport both the failure and its rescue, supporting a causal role for the carried state. This mechanism motivates Composite Directional Governance Editing (C-DGE), which amortizes the causally identified matched-state displacement: from one observed run, two mutually exclusive history-task routes predict input-dependent corrections in protected, gradient-defined low-rank subspaces. On a locked final test, C-DGE recovers 30.36% of the mismatch-induced margin gap and rescues 32 of 135 induced decision flips while leaving all 2,856 protected controls unchanged on the locked test. These results identify reversible governance-state interference as a source of task-transition errors and show that it can be repaired selectively without a counterfactual rerun.