[2605.05220] MidSteer: Optimal Affine Framework for Steering Generative Models
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Computer Science > Machine Learning
arXiv:2605.05220 (cs)
[Submitted on 17 Apr 2026]
Title:MidSteer: Optimal Affine Framework for Steering Generative Models
Authors:Tatiana Gaintseva, Andrew Stepanov, Ziquan Liu, Martin Benning, Gregory Slabaugh, Jiankang Deng, Ismail Elezi
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Abstract:Steering intermediate representations has emerged as a powerful strategy for controlling generative models, particularly in post-deployment alignment and safety settings. However, despite its empirical success, it currently lacks a comprehensive theoretical framework. In this paper, we bridge this gap by formalizing the theory of concept steering. First, we establish a link between steering and affine concept erasure, proving that the standard approach for removing unwanted behaviors is a special case of LEACE (a closed-form method for affine erasure). Next, we formulate a principled theoretical framework for concept switching, LEACE-Switch, and characterize the assumptions under which it provides an optimal affine solution. Building on this analysis, we then introduce MidSteer (Minimal Disturbance concept Steering), a more general affine framework for concept manipulation that relaxes these assumptions and enables directed, minimal-disturbance transformations. We demonstrate that MidSteer performs favorably across a range of tasks, modalities, and architectures, including vision diffusion models and large language models.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.05220 [cs.LG]
(or arXiv:2605.05220v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.05220
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arXiv-issued DOI via DataCite
Submission history
From: Tatiana Gaintseva [view email] [v1] Fri, 17 Apr 2026 19:23:33 UTC (7,561 KB)
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