SWE-bench Science:编码智能体难解科学缺陷

Rohan Paul · @rohanpaul_ai · X·2026-08-25 11:14·12分钟前
AI 导读

新基准 SWE-bench Science 用开源科学仓库的真实缺陷测试编码智能体,发现它们擅长消除可见故障,却难以恢复底层科学逻辑,且领域知识帮助有限。最佳配置 Claude Code 配 Opus-5 通过 96.64% 公开测试,但 Pass@1 仅 47.90%。瓶颈在于验证而非知识:智能体无法将科学解释与可执行证据对照时,会锚定解释而非检验它。

Rohan Paul@rohanpaul_ai
36AI 编辑部评分,满分 100

SWE-bench Science:编码智能体难解科学缺陷

2026-08-25 11:14· 12分钟前
AI 导读

新基准 SWE-bench Science 用开源科学仓库的真实缺陷测试编码智能体,发现它们擅长消除可见故障,却难以恢复底层科学逻辑,且领域知识帮助有限。最佳配置 Claude Code 配 Opus-5 通过 96.64% 公开测试,但 Pass@1 仅 47.90%。瓶颈在于验证而非知识:智能体无法将科学解释与可执行证据对照时,会锚定解释而非检验它。

Coding agents fix the symptom you show them far more often than the defect underneath.

Agents are good at making a visible failure disappear, but this paper finds them much weaker at restoring the science underneath, and supplied domain knowledge does not reliably help.

SWE-bench Science gives agents real defects from open scientific repositories and scores public tests they can iterate against separately from private tests they never see.

The best configuration, Claude Code with Opus-5, clears 96.64% of public tests but reaches 47.90% Pass@1, which requires every hidden test to pass.

So the limit looks like verification rather than knowledge: an agent that cannot check supplied science against executable evidence can anchor on the explanation instead of testing it.

– arxiv. org/abs/2608.19799

Title: "SWE-bench Science: Can Coding Agents Resolve Engineering Tasks in Science?"

来源:Rohan Paul· x.com