Controlling Tool Use with Heading-Specific Activation Steering

2026-07-06

AI Summary

This work addresses the prevalent issue in tool-augmented large language models (LLMs) of unnecessarily frequent external tool invocations even when tools are not required, reflecting a lack of precise control over tool-calling behavior. The authors propose a novel method that extracts activation steering vectors anchored at specific header positions in the input context. This approach reveals, for the first time, that although tool usage lacks explicit parametric encoding, it can be bidirectionally and causally modulated through activation vectors at context-dependent locations. Through activation steering, geometric representation analysis, and extensive cross-model and cross-domain experiments, the method significantly suppresses redundant tool calls across five open-source LLMs and three task domains. The findings demonstrate that internal representations governing tool use exhibit nonlinear, multimodal, and tool-type-specific characteristics, enabling precise and effective regulation of tool-invocation behavior.

Abstract

Tool-augmented large language models extend their capabilities beyond parametric knowledge through external tools, but tend to invoke them unnecessarily. We investigate whether tool-use decisions have any stable internal representation that can be extracted and manipulated, a question that is non-trivial given that tools exist entirely in context at inference time and have no direct encoding in model weights. We show that steering vectors extracted from heading-anchors positions exert bidirectional causal control over tool-invocation behavior across five open-source models and three domains, suppressing unnecessary tool use most effectively in domains where parametric reasoning suffices. However, geometric analysis reveals that this causal effectiveness does not correspond to clean linear structure: tool-invocation steps exhibit diffuse, bimodal alignment with the suppression vector rather than the consistent negative alignment a linear encoding account would predict, and different tool types recruit largely distinct internal signatures with low cross-tool feature overlap. We hypothesize these geometric properties are indicative of the non-parametric nature of tools, and distinguish tool-use steering vectors from those extracted for parametrically grounded concepts. The relationship between this geometric irregularity and the observed causal effectiveness remains an open question.

Problem

• tool use
• large language models
• internal representation
• activation steering
• non-parametric tools

Innovation

• activation steering
• tool use control
• non-parametric reasoning
• geometric representation
• heading-anchor