Steering Latent Traits, Not Learned Facts: An Empirical Study of Activation Control Limits
- URL: http://arxiv.org/abs/2511.18284v1
- Date: Sun, 23 Nov 2025 04:28:41 GMT
- Title: Steering Latent Traits, Not Learned Facts: An Empirical Study of Activation Control Limits
- Authors: Tetiana Bas, Krystian Novak,
- Abstract summary: Large language models (LLMs) require precise behavior control for safe and effective deployment across diverse applications.<n>We focus on the question of how steering effectiveness varies across different behavior types and whether the nature of target behaviors can predict steering success.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) require precise behavior control for safe and effective deployment across diverse applications. Activation steering offers a promising approach for LLMs' behavioral control. We focus on the question of how steering effectiveness varies across different behavior types and whether the nature of target behaviors can predict steering success. We address this through empirical analysis of activation steering across 50 behaviors that span persona archetypes, personality traits, misalignment behaviors, style cues, and impersonation of public figures. We present a set of comprehensive experiments on coefficient optimization, vector properties, and data requirements to provide comprehensive guidance for the implementation of activation steering. Our analysis demonstrates that steering effectiveness varies significantly by behavior type, with different behavioral categories exhibiting distinct response patterns to intervention strength. We find that trait expression follows an inverted-U curve with a steering coefficient strength. We also show that vector separation metrics do not predict steering success, but larger training datasets enable more aggressive steering. These findings provide empirically grounded guidance for implementing activation steering and demonstrate that steering effectiveness is heavily influenced by behavior type.
Related papers
- Understanding Unreliability of Steering Vectors in Language Models: Geometric Predictors and the Limits of Linear Approximations [0.0]
I investigate why steering reliability differs across behaviors and how it is impacted by steering vector training data.<n>I find that higher cosine similarity between training activation differences predicts more reliable steering.<n>I observe that behavior datasets where positive and negative activations are better separated along the steering direction are more reliably steerable.
arXiv Detail & Related papers (2026-02-19T22:37:05Z) - AMPS: Adaptive Modality Preference Steering via Functional Entropy [66.69992693275061]
We introduce an instance-aware diagnostic metric that quantifies each modality's information contribution and reveals sample-specific susceptibility to steering.<n> Experimental results show that our instance-aware steering outperforms conventional steering in modulating modality preference.
arXiv Detail & Related papers (2026-02-13T02:29:06Z) - Steering Language Models Before They Speak: Logit-Level Interventions [9.055997973281919]
We propose a training-free inference-time logit intervention for controllable generation.<n>Our results show that statistically grounded logit steering can achieve large, consistent, and multi-task control gains.
arXiv Detail & Related papers (2026-01-16T03:00:33Z) - Linear Personality Probing and Steering in LLMs: A Big Five Study [0.7933052462113936]
We investigate whether linear directions aligned with the Big Five personality traits can be used for probing and steering model behavior.<n>Our results suggest that linear directions aligned with trait-scores are effective probes for personality detection.
arXiv Detail & Related papers (2025-12-19T14:41:09Z) - KV Cache Steering for Controlling Frozen LLMs [80.50365534625438]
cache steering is a lightweight method for implicit steering of language models.<n>We apply cache steering to induce chain-of-thought reasoning in small language models.
arXiv Detail & Related papers (2025-07-11T17:59:36Z) - Understanding (Un)Reliability of Steering Vectors in Language Models [21.33093425619501]
This paper studies the influence of prompt types and the geometry of activation differences on steering reliability.<n>We find that all seven prompt types used in our experiments produce a net positive steering effect, but exhibit high variance across samples, and often give an effect opposite of the desired one.
arXiv Detail & Related papers (2025-05-28T17:53:31Z) - Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms [71.85633762642125]
The vast number of parameters in models often results in highly intertwined internal representations.<n>Recent research has explored the use of sparse autoencoders (SAE) to disentangle knowledge in high-dimensional spaces for steering.<n>We propose Steering Target Atoms (STA), a novel method that isolates and manipulates disentangled knowledge components to enhance safety.
arXiv Detail & Related papers (2025-05-23T17:59:18Z) - Control-ITRA: Controlling the Behavior of a Driving Model [14.31198056147624]
We introduce a method called Control-ITRA to influence agent behavior through waypoint assignment and target speed modulation.<n>We demonstrate that our method can generate controllable, infraction-free trajectories while preserving realism in both seen and unseen locations.
arXiv Detail & Related papers (2025-01-17T03:35:11Z) - Analyzing the Generalization and Reliability of Steering Vectors [8.253773195379166]
We show that steering vectors have substantial limitations both in- and out-of-distribution.<n>In-distribution, steerability is highly variable across different inputs.<n>Out-of-distribution, while steering vectors often generalise well, for several concepts they are brittle to reasonable changes in the prompt.
arXiv Detail & Related papers (2024-07-17T08:32:03Z) - Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization [34.05163996072159]
"steering vectors" are extracted from the activations of human preference data.
This work proposes an innovative approach that could produce more effective steering vectors through bi-directional preference optimization.
Our method is designed to allow steering vectors to directly influence the generation probability of contrastive human preference data pairs.
arXiv Detail & Related papers (2024-05-28T05:10:40Z) - ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization [52.5587113539404]
We introduce a causality-aware entropy term that effectively identifies and prioritizes actions with high potential impacts for efficient exploration.
Our proposed algorithm, ACE: Off-policy Actor-critic with Causality-aware Entropy regularization, demonstrates a substantial performance advantage across 29 diverse continuous control tasks.
arXiv Detail & Related papers (2024-02-22T13:22:06Z) - Control-Aware Prediction Objectives for Autonomous Driving [78.19515972466063]
We present control-aware prediction objectives (CAPOs) to evaluate the downstream effect of predictions on control without requiring the planner be differentiable.
We propose two types of importance weights that weight the predictive likelihood: one using an attention model between agents, and another based on control variation when exchanging predicted trajectories for ground truth trajectories.
arXiv Detail & Related papers (2022-04-28T07:37:21Z) - Generative Adversarial Reward Learning for Generalized Behavior Tendency
Inference [71.11416263370823]
We propose a generative inverse reinforcement learning for user behavioral preference modelling.
Our model can automatically learn the rewards from user's actions based on discriminative actor-critic network and Wasserstein GAN.
arXiv Detail & Related papers (2021-05-03T13:14:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.