On the Identifiability of Steering Vectors in Large Language Models

• URL: http://arxiv.org/abs/2602.06801v1
• Date: Fri, 06 Feb 2026 15:53:50 GMT
• Title: On the Identifiability of Steering Vectors in Large Language Models
• Authors: Sohan Venkatesh, Ashish Mahendran Kurapath,
• Abstract summary: Activation steering methods are widely used to control large language model behavior.<n>This interpretation implicitly assumes steering directions are identifiable and uniquely recoverable from input-output behavior.<n>We prove that steering vectors are fundamentally non-identifiable due to large equivalence classes of behaviorally indistinguishable interventions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Activation steering methods, such as persona vectors, are widely used to control large language model behavior and increasingly interpreted as revealing meaningful internal representations. This interpretation implicitly assumes steering directions are identifiable and uniquely recoverable from input-output behavior. We formalize steering as an intervention on internal representations and prove that, under realistic modeling and data conditions, steering vectors are fundamentally non-identifiable due to large equivalence classes of behaviorally indistinguishable interventions. Empirically, we validate this across multiple models and semantic traits, showing orthogonal perturbations achieve near-equivalent efficacy with negligible effect sizes. However, identifiability is recoverable under structural assumptions including statistical independence, sparsity constraints, multi-environment validation or cross-layer consistency. These findings reveal fundamental interpretability limits and clarify structural assumptions required for reliable safety-critical control.

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