A Closed-Loop Modular Language Agent with Step Verification and Local Correction for Multi-Step Task Solving

Multi-step task solving with large language models in intelligent electronic systems and interactive environments requires stronger process control and execution reliability. To address local error accumulation during multi-step execution, this paper proposes a closed-loop modular language agent framework integrating task planning, action execution, step verification, local regeneration, and replanning. A process-supervision data construction method is further introduced, in which real execution steps are retained as valid samples and invalid samples are automatically synthesized through action substitution, input perturbation, observation replacement, and subgoal mismatch, providing step-level supervision for validity prediction. In the proposed framework, step verification functions as a process-level control signal that supports hierarchical recovery through local regeneration and replanning, rather than as a standalone filtering module. Experiments are conducted on mathematical reasoning and web interaction tasks. On mathematical reasoning tasks, the proposed framework achieves an accuracy of 0.650, compared with 0.317 for Integrated Training, 0.460 for CoT Training, 0.568 for ReFT, and 0.617 for Agent Lumos. On web interaction tasks, the proposed framework achieves a step success rate of 0.424, compared with 0.246 for Integrated Training and 0.310 for Agent Lumos. Among the cases where recovery is triggered, local regrounding succeeds in 85.7% of reground attempts, while replanning succeeds in 53.3% of replanning attempts. These results indicate that the proposed framework improves process stability and recovery capability in multi-step task solving.