Tool Calling is Linearly Readable and Steerable in Language Models
Language-model agents can take real actions by calling tools, so choosing the wrong tool can cause errors that are difficult to undo. Most evaluations only observe the tool choice after the model generates a call. We read this choice from the model before generation, and we steer it. For each tool, we average the model's hidden states from a few example requests to obtain a tool vector. Comparing a new request with these tool vectors predicts which tool it needs. The difference between two tool vectors gives a steering direction that can move the model toward a chosen tool without retraining. Across eight instruction-tuned models from 4B to 27B parameters, steering changes the generated c...
| Authors | Zekun Wu, Ze Wang, Seonglae Cho et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 May 2026 |
| Last updated at source | 26 September 2026 |
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