Steering Vector Fields for Context-Aware Inference-Time Control in Large Language Models

2026-02-02

AI Summary

This work addresses the limitations of traditional steering vectors in long-form text generation and multi-attribute control, where static vectors fail to adapt to contextual variations, leading to degraded reliability. To overcome this, the authors propose Steering Vector Fields (SVF), a novel approach that introduces the concept of vector fields into large language model inference control. SVF employs a differentiable concept scoring function to dynamically compute local gradients at each hidden activation, thereby constructing context-aware steering directions. This framework enables coordinated multi-layer intervention and unified control over multiple attributes. Extensive experiments across diverse models and tasks demonstrate that SVF significantly enhances both control strength and reliability, effectively mitigating control failure in scenarios involving long sequences and complex attribute specifications.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Steering vectors (SVs) offer a lightweight way to control large language models (LLMs) at inference time by shifting hidden activations, providing a practical middle ground between prompting and fine-tuning. Yet SVs can be unreliable in practice. Some concepts are unsteerable, and even when steering helps on average it can backfire for a non-trivial fraction of inputs. Reliability also degrades in long-form generation and multi-attribute steering. We take a geometric view of these failures. A static SV applies the same update vector everywhere in representation space, implicitly assuming that the concept-improving direction is constant across contexts. When the locally effective direction varies with the current activation, a single global vector can become misaligned, which yields weak or reversed effects. Guided by this perspective, we propose Steering Vector Fields (SVF), which learns a differentiable concept scoring function whose local gradient defines the steering direction at each activation, making interventions explicitly context-dependent. This formulation supports coordinated multi-layer interventions in a shared, aligned concept space, and enables efficient long-form and multi-attribute control within a unified framework. Across multiple LLMs and steering tasks, SVF delivers stronger and more reliable control, improving the practicality of inference-time steering.

Problem

• steering vectors
• context-aware control
• large language models
• inference-time intervention
• representation space

Innovation

• Steering Vector Fields
• context-aware control
• inference-time intervention
• concept scoring function
• large language models