Steering Vectors for Competing Ethical Frameworks Are Interchangeable: A Competing-Target Test for Activation Steering
On the map: Mechanistic interpretability → Probing deception, bias and beliefs also fits: Activation steering methods
Abstract (arXiv)
Activation steering adds a vector to a model's hidden states to control a named behaviour. A gain on that behaviour, however, does not show that the vector controls it directly: it may act through a component shared with neighbouring behaviours, which unsteered and random baselines cannot separate. We introduce the competing-target test: a vector is compared with the vector built the same way for a competing target, and an equivalence test states when their difference is below a useful margin. We apply it in open-weight models to vectors for Kantian deontology, which judges an act by the rule it follows, and act utilitarianism, which judges it by the welfare it produces. On held-out situations, prompting a framework raises its score and lowers the other's, whereas vectors for either framework move both scores alike, and none reaches the margin. The two vectors share most of their direction, a request to apply some framework; the same construction separates an optimistic from a pessimistic tone, whose vectors share much less. What a vector shares with its competitors therefore sets what it controls: steering a perspective can deliver a mixture, and measuring a disposition can measure its neighbours, unless tested against the competing target.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Probing deception, bias and beliefs, one of 13 directions in Mechanistic interpretability. Builds on 22 of its 52 references that are in the map. First posted on arXiv 14 Oct 2026.
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- 2023Steering Language Models With Activation Engineeringcited by 922
- 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 876
- 2019Designing and Interpreting Probes with Control Taskscited by 640
- 2025Persona Vectors: Monitoring and Controlling Character Traits in Language Modelscited by 345
- 2025AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoderscited by 218
- 2023Evaluating the Moral Beliefs Encoded in LLMscited by 215
- 2024Improving Instruction-Following in Language Models through Activation Steeringcited by 160
- 2023Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patchingcited by 80
9 more
- 2024RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representationscited by 79
- 2023Improving Activation Steering in Language Models with Mean-Centringcited by 68
- 2025Understanding (Un)Reliability of Steering Vectors in Language Modelscited by 47
- 2025A Unified Understanding and Evaluation of Steering Methodscited by 46
- 2025Steering Evaluation-Aware Language Models to Act Like They Are Deployedcited by 35
- 2025MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomescited by 23
- 2020A Selective Review of Negative Control Methods in Epidemiologycited by 16
- 2024Exploring and steering the moral compass of Large Language Modelscited by 16
- 2026Steering Safely or Off a Cliff? Rethinking Specificity and Robustness in Inference-Time Interventionscited by 6
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