Tool Calling is Linearly Readable and Steerable in Language Models
📰 ArXiv cs.AI
Researchers found that tool calling in language models is linearly readable and steerable, allowing for controlled tool selection by modifying internal activations.
Action Steps
- Probe instruction-tuned models to identify tool selection patterns
- Analyze internal activations of tools to determine mean-difference
- Modify internal activations to steer tool selection
- Test the effectiveness of tool selection control using evaluation metrics
- Apply this technique to improve the reliability of language models in real-world applications
Who Needs to Know This
This research benefits AI engineers and researchers working with language models, as it provides insights into controlling tool selection and improving model reliability.
Key Insight
💡 Tool selection in language models can be controlled by modifying internal activations, allowing for more reliable and efficient model performance.
Share This
🚀 Tool calling in language models is linearly readable and steerable! 🤖 Researchers find that modifying internal activations can control tool selection. #AI #LanguageModels
Key Takeaways
Researchers found that tool calling in language models is linearly readable and steerable, allowing for controlled tool selection by modifying internal activations.
Full Article
Title: Tool Calling is Linearly Readable and Steerable in Language Models
Abstract:
arXiv:2605.07990v1 Announce Type: cross Abstract: When a tool-calling agent picks the wrong tool, the failure is invisible until execution: the email gets sent, the meeting gets missed. Probing 12 instruction-tuned models across Gemma 3, Qwen 3, Qwen 2.5, and Llama 3.1 (270M to 27B), we find the identity of the chosen tool is linearly readable and steerable inside the model. Adding the mean-difference between two tools' average internal activations switches which tool the model selects at 77-100
Abstract:
arXiv:2605.07990v1 Announce Type: cross Abstract: When a tool-calling agent picks the wrong tool, the failure is invisible until execution: the email gets sent, the meeting gets missed. Probing 12 instruction-tuned models across Gemma 3, Qwen 3, Qwen 2.5, and Llama 3.1 (270M to 27B), we find the identity of the chosen tool is linearly readable and steerable inside the model. Adding the mean-difference between two tools' average internal activations switches which tool the model selects at 77-100
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