近期研究突显了AI辅助科学同行评审系统存在的重大漏洞。研究表明，AI评审员可能通过仅展示性的修改（例如更改摘要或表述方式）而被操纵，而无需改变核心科学内容。这些攻击可能导致评分虚高和接受率增加，引发担忧，即作者可能会为了迎合AI的判断而牺牲科学价值。此外，多模态AI评审员容易受到针对图表和文本的攻击，这需要强大的防御措施和谨慎的人工监督来维护同行评审过程的完整性。 AI
影响 强调了在科学评估中需要强大的AI系统，以防止操纵并确保完整性。
排序理由 多篇研究论文详细介绍了AI辅助科学同行评审中的漏洞和潜在防御措施。
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arXiv:2606.13044v1 Announce Type: new Abstract: As AI-generated reviews move from experimental tools into peer-review infrastructure, most robustness concerns have focused on explicit attacks such as hidden instructions and prompt injection. We study a harder and more policy-rele…
arXiv:2606.12716v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) and Multimodal LLMs (MLLMs) into scientific peer-review workflows introduces novel and significant risks for adversarial manipulation, especially given the multimodal nature of scienti…
arXiv:2606.13349v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise in automating scientific peer review. However, existing approaches often struggle to generate in-depth reviews supported by concrete evidence. We argue that a key limitation is the lac…
Large language models (LLMs) have shown promise in automating scientific peer review. However, existing approaches often struggle to generate in-depth reviews supported by concrete evidence. We argue that a key limitation is the lack of flexibility to proactively investigate susp…
Large language models (LLMs) have shown promise in automating scientific peer review. However, existing approaches often struggle to generate in-depth reviews supported by concrete evidence. We argue that a key limitation is the lack of flexibility to proactively investigate susp…
As AI-generated reviews move from experimental tools into peer-review infrastructure, most robustness concerns have focused on explicit attacks such as hidden instructions and prompt injection. We study a harder and more policy-relevant failure mode: no hidden text, no prompt inj…
arXiv:2506.08134v4 Announce Type: replace Abstract: Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale. Exponential growth in manuscript submissions to premier ML venues such as NeurIPS, ICML, and ICLR is outpacing the fi…
arXiv:2606.10159v1 Announce Type: cross Abstract: AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage. Although such systems promise to reduce reviewer burden and accelerate publication, their robustness to…