arXiv:2607.05790v1 Announce Type: new 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 acr
The proliferation of tool-augmented language models necessitates more refined control mechanisms to overcome limitations like unnecessary invocations and improve efficiency.
This research demonstrates a novel method for directly manipulating tool-use decisions in LLMs, which is critical for developing more reliable and sophisticated AI agents.
The ability to causally control tool invocation via steering vectors introduces a new paradigm for fine-grained influence over LLM behavior, moving beyond simple prompt engineering.
- · AI developers
- · Businesses deploying AI agents
- · AI-as-a-Service providers
- · Inefficient AI agent systems
- · Those relying solely on passive prompt techniques
More efficient and reliable autonomous AI agents will emerge, reducing operational costs and increasing utility.
This improved control could facilitate the integration of AI agents into sensitive or safety-critical applications.
The development of a 'control layer' for agentic behavior across diverse models might accelerate the capabilities of AI to mimic human-like strategic decision-making.
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Read at arXiv cs.AI