Controlling Tool Use with Heading-Specific Activation Steering

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 bidirectio

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ethics.ai (7 July 2026), “Controlling Tool Use with Heading-Specific Activation Steering,” evidence record 178, https://ethics.ai/record/178 (originally published by arXiv).

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