PLAN AROUND CHANGES, LEARN FROM VIOLATIONS: PERSISTENT TASK STATE FOR LONG-HORIZON VLA AGENTS

Failure is not a diagnosis. In a failed long-horizon robot task, the user may have changed the goal, the world may have invalidated a grounding, the planner may be following an obsolete goal occurrence, or the policy may simply have executed poorly. Two common adaptation strategies reconstruct task state from accumulated history or train on failed trajectories. The first can forget commitments that still hold; the second can learn from events for which the agent was never responsible. We introduce CoPE, a two-timescale adaptation framework built around persistent task state. It gives each commitment a stable occurrence identity and tracks whether that occurrence remains authoritative and grounded. At runtime, CoPE revises affected commitments and recompiles the current planning problem. Across episodes, violations of still-valid commitments attach corrective supervision to the same occurrence; legitimate task and world changes remain state transitions rather than training targets. Across complementary controlled, simulator, and learned-policy studies, the same task-side state is exercised through both time scales. In controlled state scaling, local persistent updates remain stable as the state grows from 4 to 16 to 32 commitments, while full-state regeneration falls from 30/36 to 13/36 to 8/36 valid transitions; at 32 commitments, 27/28 full-state errors contain preservation drift. On 1,200 prospective records, occurrence-level responsibility improves the respective routing metrics by 6.67–11.50 percentage points over episode-level, terminal-skill, and flat-trace baselines. We further carry occurrence identity through actual OpenVLA adapter updates and occurrence-gated deployment, where all 18 dynamic runs switch at the designated task occurrence with exact planning and no history corruption or invalid transaction. Persistent commitments therefore tell an agent what to re-plan now and which component—if any—should learn later.