RaCE: Urban Spatial Multi-Hop Reasoning via Route-Aware Cumulative Evidence

Urban spatial multi-hop reasoning combines visual observations and road geometry to infer complete routes and spatial answers from sequential instructions. Each location choice changes the spatial context and evidence available at subsequent hops. Similar buildings and road patterns can sustain local self-consistency along incorrect routes, making early errors difficult to detect through stepwise checks. Comparing these routes requires evaluating evidence from different spatial contexts on a common scale. To address these challenges, we propose Route-Aware Cumulative Evidence (RaCE). RaCE maintains feasible candidate routes and dynamically updates spatial contexts according to each branch's decision history. It integrates visual and geometric evidence into support scores comparable across branches and accumulates this evidence along complete routes, allowing later information to inform the reassessment of earlier decisions. The framework selects the best-supported route and final spatial answer through route-level comparison and answer-level evidence aggregation, respectively. Adaptive perception then leverages score bounds to acquire visual evidence selectively, yielding the same route and spatial answer as full acquisition. Experiments across five cities and four general-purpose models show that RaCE achieves mean route and answer accuracies of 77.7% and 92.1%, respectively, while reducing inference latency by 67.7% on average compared with existing methods. The data and code are available at https://anonymous.4open.science/r/RaCE-ECF0.