Google 新论文提出 PLE 基准：最强智能体在实体工程任务上仅得 50.6%
New Google paper finds that the best AI agent scores only 50.6% on physical engineering jobs, often by missing a hard safety or feasibility rule.
Google 新论文提出 PLE 基准，包含 85 个由执业工程师编写或核验的电气、土木、结构和机械工程任务，最强智能体仅得 50.6%。Claude Opus 5.5 在 Claude Code 中以 50.6% 在 14 个模型中居首，每个任务消耗 27M tokens、耗时 63 分钟，需阅读约 61 个项目文件并交付进度表、报告或 2D/3D 图纸。在设计任务中其输出满足 91% 的约束，但只有 41.3% 满足全部硬性约束。
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Google 的 PLE 基准用 85 个真实工程任务测出智能体在硬性安全约束上的短板，对做工程智能体的人有直接参考。
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New Google paper finds that the best AI agent scores only 50.6% on physical engineering jobs, often by missing a hard safety or feasibility rule.
Engineering benchmarks for LLMs mostly test textbook questions or toy problems that top agents already handle well. Real engineers work from piles of PDFs, spreadsheets, and CAD files and must issue designs that meet code and stay cheap.
They built PLE, 85 electrical, civil, structural, and mechanical jobs written or checked by practising engineers. Agents must read about 61 project files per task and deliver schedules, reports, or 2D and 3D drawings.
Claude Opus 5.5 in Claude Code topped 14 models at 50.6%, using 27M tokens and 63 minutes per task. On design tasks its outputs met 91% of constraints, but only 41.3% met every hard constraint.
If you build engineering agents, gate every design on hard safety and code limits, and spend effort on model choice before tuning the agent scaffold.