目标持久编码智能体作为科学性能工程师：固定半径最近邻案例研究

中文摘要 AI 辅助

编码智能体可以在多次工具使用轮次中追求持久目标，但通用智能体能够进行严谨的科学性能工程研究的证据仍然有限。我们展示了一个仓库规模的案例研究，其中现成的Codex和Claude Code智能体优化了用于粒子追踪的固定半径最近邻（FRNN）搜索。从一个依赖PyTorch的CUDA实现开始，这些智能体遵循一个可执行目标，该目标指定了精确正确性测试、性能分析要求和验收标准，而不规定代码转换。在主要的顺序轨迹中，它们自主移除了PyTorch依赖，并进行了假设驱动的优化实验。由此产生的独立C++/CUDA库精确复现了目标参考结果。其同步NumPy接口相较于原始的GPU驻留PyTorch接口实现了1.6倍的加速，尽管包含了主机传输。在不同的GPU架构和软件栈上观察到了类似的加速。一次独立的优化重跑遵循了不同的假设序列，并在目标工作负载上达到了更好的性能。这些结果表明，目标持久的编码智能体可以充当实验性性能工程师，并且可执行的科学契约既需要用于指导也需要用于验证它们的优化。

英文摘要

Coding agents can pursue persistent objectives across many tool-use turns, but evidence that general-purpose agents can conduct rigorous scientific performance engineering remains limited. We present a repository-scale case study in which off-the-shelf Codex and Claude Code agents optimize fixed-radius nearest-neighbor (FRNN) search for particle tracking. Starting from a PyTorch-dependent CUDA implementation, the agents follow an executable goal that specifies exact-correctness tests, profiling requirements, and acceptance criteria without prescribing code transformations. In the primary sequential trajectory, they autonomously remove the PyTorch dependency and conduct hypothesis-driven optimization experiments. The resulting standalone C++/CUDA library exactly reproduces the targeted reference result. Its synchronous NumPy interface achieved 1.6-fold speedup over the original GPU-resident PyTorch interface, despite including host transfers. Similar speedups were observed across different GPU architectures and software stacks. An independent optimization rerun followed a different sequence of hypotheses and reached even better performance on the target workload. These results show that goal-persistent coding agents can act as experimental performance engineers, and that executable scientific contracts are needed both to guide and to validate their optimization.

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10 pages, 2 figures