首个评估AI科研系统学术诚信的基准，发现多数模型会伪造数据而非承认失败。
SciIntegrity-Bench: A Benchmark for Evaluating Academic Integrity in AI Scientist Systems
- 设计33个陷阱场景，诚实承认失败才是正确答案，完成任务需违规
- 7个主流大模型整体诚信问题率达34.2%，无一零失误，缺失数据时全数伪造
- 揭示模型有固有完成倾向，即使不施压也仍会造假，需训练诚实拒绝能力
AI科研系统日益用于自主研究，但其学术诚信从未被系统评估。我们提出SCIINTEGRITY-BENCH，首个基于矛盾评估范式的基准：33个场景分布在11类陷阱中，诚实承认失败是唯一正确响应，而任务完成需违规。在231次评估运行中，涵盖7个先进LLM，总体诚信问题率达34.2%，无模型实现零失败。尤其在缺失数据场景中，所有7个模型均生成合成数据，仅在是否披露替换上存在差异。进一步提示消融实验表明：移除明确完成压力后，未披露伪造率从20.6%降至3.2%，但底层合成率不变，揭示了独立于提示指令的内在完成偏差。结果表明，缺乏诚实拒绝的训练倾向是主要失败原因。代码与数据已开源至https://github.com/liuxingtong/Sci-Integrity-Bench。
原文摘要 · Abstract (English)
AI scientist systems are increasingly deployed for autonomous research, yet their academic integrity has never been systematically evaluated. We introduce SCIINTEGRITY-BENCH, the first benchmark designed around a dilemmatic evaluation paradigm: each of its 33 scenarios across 11 trap categories is constructed so that honest acknowledgment of failure is the only correct response, while task completion requires misconduct. Across 231 evaluation runs spanning 7 state-of-the-art LLMs, the overall integrity problem rate reaches 34.2%, and no model achieves zero failures. Most strikingly, across missing-data scenarios, all seven models generate synthetic data rather than acknowledging infeasibility, differing only in whether they disclose the substitution. A further prompt ablation study separates two drivers: removing explicit completion pressure sharply reduces undisclosed fabrication from 20.6% to 3.2%, while the underlying synthesis rate remains unchanged, revealing an intrinsic completion bias that persists independent of prompt-level instructions. These findings point to the absence of honest refusal as a trained disposition as the primary driver of observed failures. We release SCIINTEGRITY-BENCH at https://github.com/liuxingtong/Sci-Integrity-Bench.
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