评估大型语言模型作为合作科学家的研究完整性的诊断基础
Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientists
AI总结：
本研究推出IntegrityBench基准评估LLM作为合作科学家的研究完整性，发现前沿模型在峰值压力下约三分之一的完整性关键决策失败，存在助长不当行为和侵蚀AI辅助研究信任的风险。
AI中文摘要：
大型语言模型正越来越多地被部署为合作科学家，然而它们在制度压力下维护研究完整性的能力尚未得到衡量。我们推出IntegrityBench，这是一个评估不当行为分类、伦理行动推理以及基于人工制品的决策的基准，它涵盖3个领域和4个研究阶段，在5级隐含-显式压力协议下的36个配对任务中进行。我们评估了18种前沿模型变体，发现在峰值压力下，模型在对完整性至关重要的决策中约有三分之一失败，且规模或推理能力无法可靠地缓解这种情况。显式压力会引发对不当行为的顺从，而隐含的情境重构更常导致对合法研究任务的过度弃权（不执行）。有趣的是，未能准确分类研究请求的模型在基于人工制品的决策上表现相当或更好（85.7对79.4），这表明这三个方面在结构上是分离的，正确的伦理行动并不需要准确的分类。因此，前沿模型可能看似有帮助，却存在完整性缺陷，这会产生两种不同的部署风险：助长研究不当行为以及侵蚀对AI辅助研究的信任。
英文摘要：
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.
