arXiv AI July 23, 2026 By Oshayer Siddique, J. M Areeb Uzair Alam, Md Jobayer Rahman Rafy, Syed Rifat Raiyan, Hasan Mahmud, Md Kamrul Hasan

Statistically Grounded Sparse-Feature Interventions for Activation-Space Control in Large Language Models

Read the original on arXiv AI →

arXiv:2607. 19364v1 Announce Type: new Abstract: Activation steering offers a lightweight alternative to fine-tuning for behavioral control of large language models, but SAE-based steering methods often rely on learned steering objectives or single-criterion feature selection.

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

arXiv AI

Aug 12

SAE-StatSteer: Statistical Consensus Feature Selection for Optimization-Free Activation Steering of Large Language Models

arXiv:2607. 19364v2 Announce Type: replace Abstract: Activation steering adds a residual-stream direction at inference time, providing lightweight behavioral control without fine-tuning.

By Oshayer Siddique, J. M Areeb Uzair Alam, Md Jobayer Rahman Rafy, Syed Rifat Raiyan, Hasan Mahmud, Md Kamrul Hasan

llms fine-tuning

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 Machine Learning

Aug 7

Cross-Architecture Steering Transfer in Language Models: A Systematic Empirical Study

arXiv:2608. 05164v1 Announce Type: cross Abstract: Independently trained large language models may develop shared internal representations of semantic concepts despite architectural differences -- but whether this geometric similarity has functional consequences for cross-model behavioural control remains untested.

By Ayushi Agarwal

llms fine-tuning safety

arXiv AI

Jun 9

Pre-Intervention Prediction of Sparse Autoencoder Steering Side Effects

arXiv:2606. 08365v1 Announce Type: cross Abstract: Sparse autoencoder (SAE) features are increasingly used to steer language models, but feature steering is rarely clean: the same intervention can behave inconsistently across contexts and perturb unrelated features.

By Evan Duan

llms

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 Machine Learning

Aug 4

LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering

arXiv:2604. 03532v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult.

By Sing Hieng Wong, Hassan Sajjad, A. B. Siddique

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Related stories

SAE-StatSteer: Statistical Consensus Feature Selection for Optimization-Free Activation Steering of Large Language Models

When is Your LLM Steerable?

Cross-Architecture Steering Transfer in Language Models: A Systematic Empirical Study

Pre-Intervention Prediction of Sparse Autoencoder Steering Side Effects

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

LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering