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[2602.06801] On the Non-Identifiability of Steering Vectors in Large Language Models
Summary
This paper explores the non-identifiability of steering vectors in large language models (LLMs), revealing that these vectors cannot be uniquely recovered from input-output behavior, which has implications for model interpretability and alignment.
Why It Matters
Understanding the non-identifiability of steering vectors is crucial for researchers and practitioners in AI and machine learning, as it highlights the limitations of current interpretability methods and stresses the need for more robust structural constraints to achieve reliable model alignment.
Key Takeaways
- Steering vectors in LLMs are fundamentally non-identifiable.
- Orthogonal perturbations can achieve similar effects, complicating interpretability.
- The findings emphasize the need for structural constraints in model alignment.
Computer Science > Machine Learning arXiv:2602.06801 (cs) [Submitted on 6 Feb 2026 (v1), last revised 16 Feb 2026 (this version, v2)] Title:On the Non-Identifiability of Steering Vectors in Large Language Models Authors:Sohan Venkatesh, Ashish Mahendran Kurapath View a PDF of the paper titled On the Non-Identifiability of Steering Vectors in Large Language Models, by Sohan Venkatesh and 1 other authors View PDF HTML (experimental) Abstract:Activation steering methods are widely used to control large language model (LLM) behavior and are often interpreted as revealing meaningful internal representations. This interpretation assumes steering directions are identifiable and uniquely recoverable from input-output behavior. We show that, under white-box single-layer access, steering vectors are fundamentally non-identifiable due to large equivalence classes of behaviorally indistinguishable interventions. Empirically, we show that orthogonal perturbations achieve near-equivalent efficacy with negligible effect sizes across multiple models and traits. Critically, we show that the non-identifiability is a robust geometric property that persists across diverse prompt distributions. These findings reveal fundamental interpretability limits and highlight the need for structural constraints beyond behavioral testing to enable reliable alignment interventions. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.06801 [cs.LG] (or arXiv:26...
