Learning-path requests now preserve accepted work before generation starts. If processing is interrupted, the same request can resume without starting duplicate model work or losing the learner's place.
Progress is saved as generation moves through discovery, evidence retrieval and outline creation. Clients can reconnect to the authenticated progress stream and continue from the latest public status without exposing private model history.