---
title: "VIAS: Value-Isolating Activation Steering for Multi-Value Alignment and Compliance for Organisations"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.03518v1"
permalink: "https://feed.astralor.com/items/itm_343ecbda354fac9e39d49f61"
published: "2026-11-02T17:53:26.000Z"
score: 59
channel: "ai"
tags: ["LLM","AI Agent","安全","VIAS","Value-Isolating Activation Steering","Multi-Value Alignment","Compliance","Organisations Organisations"]
---

# VIAS: Value-Isolating Activation Steering for Multi-Value Alignment and Compliance for Organisations

## 速览

Organisations increasingly deploy large language model agents that must follow organisational values. We check whether activation steering, which adds vectors to a model's internal activations during generation without …

## 关键点

- Organisations increasingly deploy large language model agents that must follow organisational values.
- We check whether activation steering, which adds vectors to a model's internal activations during generation without changing its weights, can improve compliance beyond prompting alone.
- Since most scenarios involve several values at once, we focus on multi-value steering.

## 全文

Organisations increasingly deploy large language model agents that must follow organisational values. We check whether activation steering, which adds vectors to a model's internal activations during generation without changing its weights, can improve compliance beyond prompting alone. Since most scenarios involve several values at once, we focus on multi-value steering. A simple way to build these vectors is to contrast responses that follow all values relevant to a scenario with responses that violate them. However, this gives nearly identical vectors for different values: they capture a general compliance signal rather than individual values, which limits their use when combining several values. We propose Value-Isolating Activation Steering (VIAS), which contrasts responses to the same scenario under two prompts, one listing all relevant values and one omitting a single target value. We keep a pair only if the target value changes from compliant to non-compliant and both responses still meet a minimum compliance level on the other values. We evaluate VIAS on PROTECT, a workplace compliance dataset where each scenario is tagged with relevant values from a 21-value taxonomy. VIAS reduces overlap between value vectors, making them better suited for multi-value steering, although this alone does not lead to independent control over each value. Combined with prompting, VIAS complies with every tagged value on 69.6% of scenarios for Qwen2.5-7B, 75.2% for Mistral-7B, and 72.6% for Phi-3.5-mini, outperforming prompting alone for all three models.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_343ecbda354fac9e39d49f61