@inproceedings{chu-etal-2026-behavior,
title = "Behavior Carries Over: An {AI} Sandbagging Detection Method Inspired by Honest Inertia",
author = "Chu, Junjie and
Li, Mingjie and
Chen, Zeyuan and
Leng, Ye and
Wang, Yisen and
Backes, Michael and
Zhang, Yang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2154/",
pages = "46083--46099",
ISBN = "979-8-89176-462-0",
abstract = "Reliable capability assessment is essential for governing and securely deploying advanced AI systems. However, ``AI Sandbagging,'' where models strategically suppress their true capabilities during evaluation, undermines existing assessment frameworks by making it difficult to distinguish genuine limitations from strategic concealment. To address this challenge, we propose a new black-box sandbag detection method inspired by ``honesty inertia,'' a psychological phenomenon where humans struggle to maintain deception after answering simple questions truthfully. We hypothesize that large language models exhibit an analogous effect: inserting several simple, unrelated questions, which target models should answer correctly, before the target query induces honest inertia, causing sandbag models to involuntarily increase accuracy while standard models remain stable. Through extensive experiments on multiple datasets with various model architectures, we confirm the validity of this hypothesis and establish this differential response as a reliable, model-independent sandbag detection signal. We further provide interpretability support for this phenomenon from two dimensions: theoretical derivation and hidden state analysis of models. This work offers a practical tool for enhancing AI audit and supervision."
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<abstract>Reliable capability assessment is essential for governing and securely deploying advanced AI systems. However, “AI Sandbagging,” where models strategically suppress their true capabilities during evaluation, undermines existing assessment frameworks by making it difficult to distinguish genuine limitations from strategic concealment. To address this challenge, we propose a new black-box sandbag detection method inspired by “honesty inertia,” a psychological phenomenon where humans struggle to maintain deception after answering simple questions truthfully. We hypothesize that large language models exhibit an analogous effect: inserting several simple, unrelated questions, which target models should answer correctly, before the target query induces honest inertia, causing sandbag models to involuntarily increase accuracy while standard models remain stable. Through extensive experiments on multiple datasets with various model architectures, we confirm the validity of this hypothesis and establish this differential response as a reliable, model-independent sandbag detection signal. We further provide interpretability support for this phenomenon from two dimensions: theoretical derivation and hidden state analysis of models. This work offers a practical tool for enhancing AI audit and supervision.</abstract>
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%0 Conference Proceedings
%T Behavior Carries Over: An AI Sandbagging Detection Method Inspired by Honest Inertia
%A Chu, Junjie
%A Li, Mingjie
%A Chen, Zeyuan
%A Leng, Ye
%A Wang, Yisen
%A Backes, Michael
%A Zhang, Yang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chu-etal-2026-behavior
%X Reliable capability assessment is essential for governing and securely deploying advanced AI systems. However, “AI Sandbagging,” where models strategically suppress their true capabilities during evaluation, undermines existing assessment frameworks by making it difficult to distinguish genuine limitations from strategic concealment. To address this challenge, we propose a new black-box sandbag detection method inspired by “honesty inertia,” a psychological phenomenon where humans struggle to maintain deception after answering simple questions truthfully. We hypothesize that large language models exhibit an analogous effect: inserting several simple, unrelated questions, which target models should answer correctly, before the target query induces honest inertia, causing sandbag models to involuntarily increase accuracy while standard models remain stable. Through extensive experiments on multiple datasets with various model architectures, we confirm the validity of this hypothesis and establish this differential response as a reliable, model-independent sandbag detection signal. We further provide interpretability support for this phenomenon from two dimensions: theoretical derivation and hidden state analysis of models. This work offers a practical tool for enhancing AI audit and supervision.
%U https://aclanthology.org/2026.findings-emnlp.2154/
%P 46083-46099
Markdown (Informal)
[Behavior Carries Over: An AI Sandbagging Detection Method Inspired by Honest Inertia](https://aclanthology.org/2026.findings-emnlp.2154/) (Chu et al., Findings 2026)
ACL