Dual-Stance Evaluation of Sycophancy: The Structure of Agreement and the Limits of Intervention
On the map: Mechanistic interpretability → Activation steering methods
Abstract (arXiv)
Activation steering can shift LLM behaviour, but standard evaluations do not typically test whether a sycophancy-reduction direction also suppresses agreement with factually correct statements. We introduce dual-stance evaluation, which tests both stances of each topic, and apply it to centroid-difference steering on Llama-3-8B-Instruct. We find a dissociation: the model represents sycophantic and factual agreement in geometrically distinct subspaces, yet the steering direction projects equally onto both and cannot differentially target either. The direction accordingly reduces agreement with factually correct statements (e.g. that the Earth is round) as well as sycophantic ones. All other static properties of the two activation groups are matched, suggesting the behavioural dissociation arises from generation dynamics or from finer-grained structure that residual-stream analysis cannot resolve. The pattern illustrates a general gap: representations that are readable from activations may not be writable through them.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Activation steering methods, one of 13 directions in Mechanistic interpretability. Builds on 21 of its 31 references that are in the map. First posted on arXiv 22 Apr 2026.
Builds on
21 of 31 references.
Closest ancestors · 4
Same direction, newest first
- 2026Steering at the Source: Style Modulation Heads for Robust Persona Control24 Feb
- 2024Analyzing the Generalization and Reliability of Steering Vectors17 Jul
- 2023Steering Llama 2 via Contrastive Activation Addition9 Dec
- 2023Steering Language Models With Activation Engineering20 Aug
Other references · 17
- 2022Locating and Editing Factual Associations in GPTcited by 2,691
- 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,336
- 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,302
- 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,165
- 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
- 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,032
- 2022Toy Models of Superpositioncited by 971
- 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 836
- 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 767
- 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 724
- 2023The Linear Representation Hypothesis and the Geometry of Large Language Modelscited by 716
- 2023Localizing Model Behavior with Path Patchingcited by 195
5 more
- 2023Simple synthetic data reduces sycophancy in large language modelscited by 187
- 2020Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?cited by 151
- 2024Improving Steering Vectors by Targeting Sparse Autoencoder Featurescited by 92
- 2023Overthinking the Truth: Understanding how Language Models Process False Demonstrationscited by 81
- 2026Analysing the Safety Pitfalls of Steering Vectorscited by 10
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026A Geometric Account of Activation Steering through Angle-Norm Decomposition
- 2026PCA-guided Activation Scaling for Monotonic Bidirectional Control over LLM Sycophancy
- 2026The Sharp Directions Are Against You: Curvature Analysis of Activation Steering
- 2025BASIL: Bayesian Assessment of Sycophancy in LLMs
- 2026LLMs Know They're Wrong and Agree Anyway: The Shared Sycophancy-Lying Circuit
- 2026On the Steering Dimensionality of Refusal in Language Models
- 2026Activation Steering Induces Emergent Misalignment: A More Comprehensive Evaluation
- 2026Agentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models