CCE-Mobility: Counterfactual World–Agent Co-Evolution for Mobility Agents

Large Language Model (LLM) agents are increasingly used for mobility planning and tool-based assistance, yet the world may change after a plan or commitment has already been formed. Roads may be blocked, services may become unavailable, or acquired information may become stale, requiring the agent to revise its decisions while preserving the original user need. While self-evolving agents learn from interaction experience and language world models simulate environment dynamics, an important question remains underexplored: what plausible environmental change would invalidate an already-made decision while leaving the original task achievable? We introduce CCE-Mobility, a counterfactual world–agent co-evolution framework for recovery under dynamic world changes. Given an observed trajectory, the world model reasons about decision-supporting dependencies and constructs a localized, executable intervention that invalidates one while preserving the original goal. The resulting counterfactual world provides recovery experience for agent adaptation, while persistent failures guide subsequent world-side evolution. Thus, the agent learns to recover from challenging world changes, while the world model learns to construct counterfactuals targeting unresolved weaknesses. Experiments on TravelPlanner and MobilityBench show consistent recovery gains across all evaluated backbones. On TravelPlanner, CCE-Mobility improves Recovery by 0.1092–1.0055 and reduces Low by 4.99–26.28 percentage points. Paired validation further shows an additional 4.05-point Low reduction from world-side evolution over agent-only adaptation. Code will be made publicly available at https://anonymous.4open.science/r/CCE-Mobility-4254.