arXiv Machine Learning June 9, 2026 By Tuc Nguyen, Thai Le

Beyond Linear Activation Steering: Invertible Latent Transformations for Controlling LLM Behavior

Read the original on arXiv Machine Learning →

arXiv:2606. 08454v1 Announce Type: new Abstract: Activation steering provides a lightweight inference-time mechanism for controlling large language models (LLMs) by modifying their internal activation vectors toward desired behaviors.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 13

GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs

arXiv:2507. 18043v2 Announce Type: replace-cross Abstract: Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at test time without updating model weights.

By Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal

llms fine-tuning multimodal safety

arXiv AI

Jul 29

Minimizing Targeted Activations: Input-Only Suppression of Evaluation-Awareness Latents in Large Language Models

arXiv:2607. 25907v1 Announce Type: cross Abstract: Activation steering controls model behavior by editing internal activations at inference time.

By Deepanshu Mody, Samarth Agarwal, Utkarsh Mittal, Dipesh Mahato

llms safety

arXiv AI

Jul 17

Steering Robustness into World Action Models via Mechanistic Interpretability and Optimal Control

arXiv:2607. 14943v1 Announce Type: cross Abstract: World Action Models (WAMs) enable semantically- and physically-informed control but are brittle under distribution shift.

By Jihoon Hong, Julian Skifstad, Qiyue Dai, Alice Chan, Glen Chou

safety

arXiv Machine Learning

Jun 16

High-Dimensional Random Projection for Activation Steering in Language Models

arXiv:2606. 15092v1 Announce Type: new Abstract: Activation steering has emerged as a key methodology for controlling the behavior of large language models (LLMs).

By Minh-Hieu Pham, Bach Do, Laziz Abdullaev, Tan Minh Nguyen, Khoat Than

llms benchmarks

arXiv Machine Learning

Jun 11

When is Your LLM Steerable?

arXiv:2606. 11599v1 Announce Type: cross Abstract: Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, model, and steering configuration.

By Chenrui Fan, Yize Cheng, Ming Li, Soheil Feizi, Tianyi Zhou

llms

arXiv AI

Jun 9

Activation Steering Induces Emergent Misalignment: A More Comprehensive Evaluation

arXiv:2606. 08682v1 Announce Type: cross Abstract: Activation steering has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs).

By Qi Cao, Jian Lou, Meiting Liu, Wenjie Feng, Dan Li, See-Kiong Ng, Anh Tuan Luu

llms fine-tuning safety

Related stories

GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs

Minimizing Targeted Activations: Input-Only Suppression of Evaluation-Awareness Latents in Large Language Models

Steering Robustness into World Action Models via Mechanistic Interpretability and Optimal Control

High-Dimensional Random Projection for Activation Steering in Language Models

When is Your LLM Steerable?

Activation Steering Induces Emergent Misalignment: A More Comprehensive Evaluation