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版本更新 · v2arXiv:2606.08243

用对称信念编辑构建比较性动机画像

Building Comparative Motivation Profiles with Symmetric Belief Editing

David Vella Zarb, Rustem Turtayev, Taywon Min, Jinghua Ou, Shi Feng

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作者提出“比较性动机画像”与“对称信念编辑”,通过编辑模型对事实的信念并测量行为变化,来区分对齐伪装背后是“追踪训练后果”还是“追踪研究者预期”。

cs.CL

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When a model behaves in unexpected ways, we want to know why, but its intent cannot be observed directly, and its self-reports may be rationalizations. We introduce comparative motivation profiles, which treat intent as propensity: we edit the model's beliefs about facts that a hypothesized motivation would depend on and measure how its behavior changes. To keep artifacts of the editing method from driving the result, we edit two minimally different hypotheses in the same way, with matched amplify and suppress edits; we call this symmetric belief editing. We apply it to alignment faking in four open-weight model organisms, contrasting scheming, which tracks the training consequences of compliance, with performative misalignment, which tracks what researchers expect to observe, and edit beliefs with synthetic document fine-tuning (SDF), activation steering, and prompting. Under SDF, amplifying performative beliefs raises alignment faking on every model, while scheming edits move it less or shift compliance in the monitored and unmonitored conditions alike. Steering on model organisms changes compliance along the expectation-tracking direction but mainly the reasoning along the consequence-tracking direction, and prompting agrees. In these organisms, alignment faking follows what the models infer their evaluators expect, which puts it closer to sycophancy than to deceptive alignment.