arXiv:2605.07990v2 Announce Type: replace-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. As agents take on consequential actions, one bad tool call can do real damage. We currently have no way to look inside the model and catch the mistake before it happens; this paper shows that we can. Inside the model, the choice of tool is carried by a single direction in activation space, one direction per pair of tools. Adding that direction during generation switches which tool the model picks. Acros
The rapid development and deployment of AI agents necessitates methods to ensure their reliability and safety, especially as they undertake more consequential actions.
This research provides a fundamental mechanism to diagnose and control AI agent behavior by directly manipulating internal model states, offering a unique avenue for explainability and steerability.
We now have a potential method to 'look inside' an AI model and correct tool-picking mistakes before execution, significantly enhancing agent reliability and trustworthiness.
- · AI agents developers
- · AI safety researchers
- · High-stakes industries (e.g., finance, healthcare)
- · Users of AI agents
- · Companies with unreliable agentic systems
- · Pure black-box AI approaches
The ability to steer tool choices in language models reduces errors and increases the practical applicability of AI agents in complex tasks.
Enhanced steerability could accelerate the adoption of autonomous agents across various industries, impacting white-collar workflows significantly.
This level of control could lead to new regulatory frameworks for AI systems, focusing on explainability and 'right-to-correct' mechanisms within agent operations.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG