TraceWAM: Task-State Reference-Guided Action Candidate Evaluation for Long-Horizon Robot Manipulation

World action models generate robot actions from visual and language context, yet action generation alone does not ensure that the resulting physical state remains aligned with the active stage of a long-horizon task. We study task-state alignment drift, the stage-relative accumulation of deviations in task-critical object poses, contacts, and spatial relations during closed-loop execution. To address this problem, we propose Task-State Reference-Guided Action Candidate Evaluation for World Action Models (TraceWAM), a framework for verifying candidate actions against the current physical task state before execution. TraceWAM maintains stage-wise physical references together with a recurrent progress memory, and predicts candidate-specific alignment deviations and uncertainty in an object-centric task-state space. Given a base action chunk and a bounded residual candidate, TraceWAM evaluates both under the same active reference and selects the residual only when its predicted risk and progress margins satisfy the verification criteria. During training, alternative action-conditioned outcomes are obtained by branching from shared simulator states, providing supervision for candidate-level consequence prediction without altering the deployment procedure. On RoboTwin 2.0 and LIBERO, TraceWAM achieves average success rates of 93.0% and 98.1%, outperforming Fast-WAM by 1.6 and 1.4 percentage points, respectively, and further achieves 41.6% overall success on RMBench. Task-state drift analyses and ablation studies further support the effectiveness of stage-referenced action verification for reliable multi-stage manipulation.