Structure-optional learning for proactive assistance

Proactive assistants advance shared tasks without waiting for explicit requests. Their actions must respect task prerequisites and allow parallel progress alongside a person. Task graphs encode these prerequisites and parallel branches but may be unavailable at inference. We introduce SOLPA (Structure-Optional Learning for Proactive Assistance), which trains a Planner using task-graph supervision. Completion-conditioned questions assume an unfinished step is complete while keeping the person’s current step fixed. Graph-derived answers supervise its next assistance action conditioned on completed-step history. Randomly omitting graph text during training lets the same parameters support inference with and without graphs. On ProAct-75 with predicted task state and annotated completed-step history, our 8B system achieves 33.37% step-success (SS) and 24.10% parallel-action rate (PA), improving over same-backbone Direct by 10.01 and 2.49 percentage points, respectively. Under the same scorer, the newly evaluated strong closed-source models reach at most 14.33% PA without task-specific training. With parameters fixed, the Planner retains 23.94% PA without graph text or its action-name constraint. Our code is available at https://github.com/only4anonymous/SOLPA.git.