arXiv:2605.25310v1 Announce Type: new Abstract: Tool-using LLM agents produce trajectories whose calls form a directed dependency graph: earlier tool outputs supply arguments to later calls. Whether this execution structure is represented inside the model is unknown; prior structural probes have targeted static code or chain-of-thought text, not an agent's run-time call graph. A low-capacity edge probe on the residual stream of Qwen3-32B decodes the tool-call dependency graph well above both a Hewitt--Liang random-label control and a positional baseline. A counterfactual contrast between value
This research provides direct evidence of how advanced AI models process complex procedural data, pushing the boundaries of interpretability in large language models. The growing sophistication of LLM agents necessitates deeper understanding of their internal workings.
Understanding how LLM agents represent and execute tool-call dependencies is critical for developing more reliable, controllable, and robust AI systems. This interpretability is vital for debugging, safety, and performance optimization of autonomous agents.
We now have empirical proof that LLMs internally represent the operational structure of agentic tool use, moving beyond mere contextual understanding to demonstrating underlying structural encoding. This changes how we approach training, fine-tuning, and safety mechanisms for agentic AI.
- · AI Safety Researchers
- · LLM Developers
- · AI Agent Companies
- · Interpretability Tools Providers
- · Developers relying on black-box LLM agents
- · Companies with opaque AI safety practices
Improved understanding of LLM agent internal states enables more precise development and debugging of complex AI workflows.
This interpretability could lead to more robust and trustworthy autonomous AI agents, accelerating their deployment in sensitive applications.
Enhanced model transparency fostered by such research might influence future AI regulatory frameworks, pushing for auditable internal reasoning in critical AI systems.
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Read at arXiv cs.CL