Meta 与密歇根州立大学论文：LLM 生成的推理图质量参差，误差会传导到置信度
Meta 和密歇根州立大学测了 9 个模型，发现 Qwen3-32B 答子题还不如 8B，做推理图的先去查图本身再信置信度。
Meta 与密歇根州立大学的论文指出，许多方法把问题拆成推理图再计算答案或置信度，但默认 LLM 生成的图是对的，很少验证。团队用 9 个模型测试，从只连接给定步骤到完全自主构图，逐步减少辅助。结果显示图质量主要取决于模型家族和图结构而非参数量，其中 Qwen3-32B 在子问题回答上反而不如 Qwen3-8B。论文结论是：依赖 LLM 推理图时，应先检查图本身的质量再信任其置信度输出。
New Meta + Michigan State Univ paper finds that LLM-built reasoning graphs vary widely in quality, and their errors carry into the confidence scores computed from them.
Many methods split a question into a graph of sub-questions, then compute answers or confidence on top. They assume the LLM's graph is correct, which is rarely checked.
They tested 9 models with less and less help, from wiring given steps to building the whole graph. Quality tracked model family and graph shape more than size, and Qwen3-32B answered sub-questions worse than Qwen3-8B.
If you build on LLM-made reasoning graphs, check the graph itself before trusting answers or confidence computed from it.