一句话：首次系统分析编程智能体对给定计划的遵循程度，揭示计划质量、提醒与阶段安排对任务成败的影响。
论文指出，人们常要求智能体遵循任务特定计划，却缺乏对其实际遵循程度的分析，因而无法判断任务成功究竟来自正确的策略推理，还是数据污染或基准过拟合。作者对编程智能体开展首次大规模系统性计划遵循研究：在 SWE-bench Verified 与 SWE-bench Pro 上，用四个 LLM 驱动 SWE-agent，考察八种计划变体，共分析 21,120 条轨迹。结果显示：没有显式计划时，智能体会退回到训练中内化的工作流，这些工作流往往不完整、过拟合或应用不一致；提供标准计划能提升问题解决率，周期性的计划提醒可减少违规并提高成功率；劣质计划比没有计划更伤性能；过早插入额外任务阶段会降低表现，尤其当这些阶段与模型内部解题策略不一致时。作者据此呼吁转向教模型遵循指令计划的微调范式。
Agents are commonly instructed to follow a task-specific plan for guidance.However, it is unknown to what extent agents actually follow instructed plans. Without such an analysis---determining the extent agents comply with a given plan---it is impossible to assess whether a solution was reached through correct strategic reasoning or through other means, e.g., data contamination or overfitting to a benchmark. This paper presents the first extensive, systematic analysis of plan compliance in programming agents, examining 21,120 trajectories from SWE-agent across four LLMs on SWE-bench Verified and SWE-bench Pro under eight plan variations. Without an explicit plan, agents fall back on internalized workflows during training, which are often incomplete, overfit, or inconsistently applied. Providing the standard plan improves issue resolution, and we observe that periodic plan reminders can mitigate plan violations and improve task success. A subpar plan hurts performance even more than no plan at all. Surprisingly, inserting additional task-relevant phases in the early stage can degrade performance, particularly when these phases do not align with the model’s internal problem-solving strategy. These findings call for fine-tuning paradigms that teach models to follow instructed plans, rather than encoding task-specific plans in them, so that they reason and act adaptively, rather than memorizing workflows.