{"schemaVersion":"nuwa-current-record-1","version":"2026-10-29.1","publication":"published","work":{"id":"ai-sandbagging","title":"AI Sandbagging: Language Models can Strategically Underperform on Evaluations","aliases":[],"authors":["Teun van der Weij","Felix Hofstätter","Ollie Jaffe","Samuel F. Brown","Francis Rhys Ward"],"primaryFunction":"discovery","secondaryFunctions":[],"kind":"study","sourceId":"ai-sandbagging-publication","attributionSourceId":"ai-sandbagging-publication-author-source","originalUrl":"https://arxiv.org/abs/2406.07358","publicationDate":"2024-06-11","updatedOn":"2026-10-25","areas":["deception"],"scopeNote":{"en":"Relevance is assigned to the research question and stated conditions of the work. A topic tag is not a risk rating.","zh":"风险关联依据研究问题及明确条件建立，主题标签不代表风险评级。"},"protocolIds":[],"contributions":[{"organizationId":"mats","role":"research","sourceId":"ai-sandbagging-publication-author-source","locator":"Author affiliations on the original paper title page"},{"organizationId":"imperial","role":"research","sourceId":"ai-sandbagging-publication-author-source","locator":"Author affiliations on the original paper title page"}],"summary":{"en":"Studies strategic underperformance on evaluations and methods for eliciting capabilities that ordinary testing may miss.","zh":"研究评测中的策略性低表现，以及诱发常规测试可能遗漏的能力的方法。"},"note":{"question":null,"method":null,"finding":null,"supports":null,"limits":null,"connections":null},"resources":[],"noteStatus":{"question":"limited","method":"limited","finding":"limited","supports":"limited","limits":"limited","connections":"limited"},"noteAvailability":{"en":"A substantive NUWA research note is not yet available. The repeated draft sections were removed pending review of the original methods, findings and limitations; use the original publication for the research account.","zh":"实质性的 NUWA 研究笔记尚未完成。重复草稿章节已移除，待核查原始方法、发现与限制；研究内容请以原始出版物为准。"},"noteSourceLocator":"No complete section-level method/results/limitations review completed for this note; cached original page and any chart audit retained as discovery evidence.","tags":[]},"reading":{"id":"ai-sandbagging","title":"AI Sandbagging: Language Models can Strategically Underperform on Evaluations","en":{"takeaway":"Prompted and fine-tuned models can selectively underperform while preserving other measured capabilities.","question":"Can models hide a subset of capabilities and produce a plausible lower evaluation profile?","design":"The study separates selective underperformance, target-score calibration and weaker-model emulation on multiple-choice benchmarks, using held-out data after prompt selection.","findings":["Selective suppression can generalize beyond the synthetic training data.","Target-score calibration is easier than faithfully imitating a weaker model’s full answer pattern."],"interpretation":"NUWA interpretation: an observed low score may reflect elicitation conditions; these experiments establish an ability to underperform, not an autonomous motive to do so.","limits":"Multiple-choice tasks and researcher-directed strategies do not measure spontaneous sandbagging in open-ended agent deployments.","connections":["NUWA connection: training-run-evaluations motivates access beyond final outputs; anti-scheming-training separately tests how evaluation awareness affects behavior."],"decisionRelevance":"NUWA recommendation: assess elicitation robustness before treating low dangerous-capability scores as inability evidence."},"zh":{"takeaway":"提示或微调模型可选择性压低表现，同时保留其他已测能力。","question":"模型能否隐藏部分能力，并产生看似合理的较低评测表现？","design":"研究在选择题基准上分别测试选择性压分、目标分数校准和弱模型模仿，提示选择后使用保留数据评测。","findings":["选择性抑制能够泛化到合成训练数据以外。","校准到目标分数比忠实模仿弱模型的完整答题模式更容易。"],"interpretation":"NUWA 解读：低分可能反映能力引出条件；实验确立压分能力，没有证明自主压分动机。","limits":"选择题与研究者指令策略不测量开放式智能体部署中的自发藏能。","connections":["NUWA 关联：训练过程评估主张超越最终输出访问；反图谋训练另测评估意识如何影响行为。"],"decisionRelevance":"NUWA 建议：将危险能力低分视为无能力证据前，先评估能力引出的稳健性。"},"sourceRefs":[{"sourceId":"ai-sandbagging-publication","url":"https://arxiv.org/html/2406.07358","locator":"3 Methods","version":"Selected original resource https://arxiv.org/html/2406.07358; 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Brown","Francis Rhys Ward"],"aliases":["https://doi.org/10.48550/arXiv.2406.07358","https://arxiv.org/abs/2406.07358","https://arxiv.org/pdf/2406.07358"]}]}