arxiv_ml 93% Match Research Paper AI researchers,VLM developers,multimodal AI engineers,computer vision specialists 1 week ago

SteerVLM: Robust Model Control through Lightweight Activation Steering for Vision Language Models

large-language-models › model-architecture

📄 Abstract

Abstract: This work introduces SteerVLM, a lightweight steering module designed to guide Vision-Language Models (VLMs) towards outputs that better adhere to desired instructions. Our approach learns from the latent embeddings of paired prompts encoding target and converse behaviors to dynamically adjust activations connecting the language modality with image context. This allows for fine-grained, inference-time control over complex output semantics without modifying model weights while preserving performance on off-target tasks. Our steering module requires learning parameters equal to 0.14% of the original VLM's size. Our steering module gains model control through dimension-wise activation modulation and adaptive steering across layers without requiring pre-extracted static vectors or manual tuning of intervention points. Furthermore, we introduce VNIA (Visual Narrative Intent Alignment), a multimodal dataset specifically created to facilitate the development and evaluation of VLM steering techniques. Our method outperforms existing intervention techniques on steering and hallucination mitigation benchmarks for VLMs and proposes a robust solution for multimodal model control through activation engineering.

Authors (4)

Anushka Sivakumar

Andrew Zhang

Zaber Hakim

Chris Thomas

Submitted

October 30, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

SteerVLM introduces a lightweight activation steering module for Vision-Language Models (VLMs) that enables fine-grained, inference-time control over outputs without modifying model weights. It learns from latent embeddings to dynamically adjust activations, preserving performance on off-target tasks and requiring only 0.14% of the original VLM's parameters. This offers a robust and efficient way to steer VLM behavior.

Business Value

Enables the creation of more controllable and reliable multimodal AI applications, such as image generation tools that precisely follow user prompts or visual assistants that adapt their responses based on nuanced instructions.

Paper Metadata

Innovation Type

Methodology and Architecture Component

Deployment Feasibility

The lightweight nature of the steering module and its inference-time operation make it highly feasible for integration into existing VLM deployment pipelines.

Limitations Addressed

Addresses the challenge of controlling VLM outputs to better adhere to desired instructions, particularly for complex semantic nuances. It overcomes the need for extensive fine-tuning or weight modification by offering a lightweight, inference-time steering mechanism.

Performance Gains

gains model control through dimension-wise activation modulation,adaptive steering across layers,preserves performance on off-target tasks

Technical Tags

Vision-Language Models (VLMs)model controlactivation steeringlatent embeddingsinference-time controllightweight moduledimension-wise modulationadaptive steeringVNIA dataset

Research Topics

Controllable generationMultimodal AIVLM interpretabilityEfficient model adaptation

Methods & Architectures

Activation steeringLearning from latent embeddingsDimension-wise activation modulationAdaptive steering across layersTraining a lightweight steering module Vision-Language Models (VLMs)steering module

Applications & Tasks

Image captioning Visual question answering Multimodal generation Content creation achieving fine-grained control over VLM outputspreserving off-target task performanceenabling inference-time control without weight modification instruction followingcontrolling VLM behaviorgenerating contextually relevant multimodal outputs

Datasets & Benchmarks

Datasets

VNIA (Visual Narrative Intent Alignment) dataset

model control accuracyperformance on off-target tasks

Related Fields

Computer VisionNatural Language ProcessingMultimodal LearningDeep LearningModel Control

Keywords

Vision-Language ModelsVLMsmodel controlactivation steeringinference-time controllightweight modulemultimodal AIVNIA datasetcontrollable generationlatent embeddings

Academic Context

#Controllable generation#Multimodal AI#VLM interpretability#Efficient model adaptation

Commercial Potential

Potential Products

Controllable image/video generation toolsAdvanced visual assistantsAI-powered content creation platforms

Target Industries

Media and EntertainmentTechnologyAdvertisingDesign

Use Case Examples

Generating images that precisely match a detailed textual description.Creating video captions that capture specific nuances of the visual content.Developing AI that can follow complex, multi-step instructions involving visual elements.

Competitive Edge

Offers a novel and efficient method for controlling VLMs at inference time, distinguishing itself from methods requiring extensive fine-tuning or architectural changes, and enabling more dynamic and precise VLM behavior.

Market Opportunity

Growing market for controllable generative AI and multimodal applications.

Revenue Models

Licensing of steering modulesintegration services for VLM applications.

Resource Requirements

Compute Needs

Moderate for training the steering module; low for inference.

Data Requirements

Requires a specialized multimodal dataset (VNIA) for training the steering module.

Deployment Constraints

The effectiveness of steering depends on the quality of the VNIA dataset and the underlying VLM architecture.

Scalability

The lightweight nature of the steering module suggests good scalability for deployment across various VLM architectures.

Production Readiness

Maturity Level

Research/Experimental

Time to Market

1-3 years for integrating into VLM-based products.

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