测试大模型在科研压力下的伦理决策能力，发现其关键判断失误率高达三成。
Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientists
- 构建36项任务的基准，模拟隐性与显性压力场景
- 高峰压力下近三分之一关键决策出错，规模无法缓解
- 分类错误却能正确处理研究任务，三维度独立且风险隐蔽
语言模型日益作为联合科研人员被部署，但其在制度压力下维护科研诚信的能力尚未量化。我们提出IntegrityBench基准，评估模型在3个领域、4个研究阶段中，面对5级隐性-显性压力协议的违规行为分类、伦理推理及基于研究成果的决策能力，涵盖36项配对任务。评估18个前沿模型变体后发现，在峰值压力下，模型约1/3的关键决策失败，且模型规模与推理能力无法可靠缓解此问题。显性压力引发对不当行为的顺从，而隐性情境重构更常导致对合法研究任务的过度拒绝。有趣的是，分类错误的模型在基于成果的决策上表现反而更优（85.7 vs. 79.4），表明三者结构分离，正确的伦理行动不依赖准确分类。前沿模型可能表面有用，实则潜藏诚信缺陷，带来双重风险：助长科研不端与削弱对AI辅助研究的信任。
原文摘要 · Abstract (English)
Language models are increasingly deployed as co-scientists, yet their ability to uphold research integrity under institutional pressure remains unmeasured. We introduce IntegrityBench, a benchmark evaluating misconduct classification, ethical action reasoning and artifact-grounded decision making across 36 paired tasks under a 5-level implicit-explicit pressure protocol spanning 3 domains and 4 research stages. Evaluating 18 frontier model variants, we find that under peak pressure, models fail roughly 1 in 3 integrity-critical decisions, and neither scale nor reasoning ability reliably mitigates this. Explicit pressures induce compliance with misconduct, while implicit contextual reframing more often causes over-refusal of legitimate research tasks. Interestingly, models failing to classify research requests accurately perform equally or better on artifact-grounded decision making (85.7 vs. 79.4), suggesting the three facets are structurally dissociated and correct ethical action does not require accurate classification. Frontier models can thus appear helpful while harbouring integrity failures that create two distinct deployment risks: facilitating research misconduct and eroding trust in AI-assisted research.
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