arXiv:2606.16813v1 Announce Type: new Abstract: Tool-augmented LLM agents rely on runtime filtering to decide which tools should be visible at each step. Causal Minimal Tool Filtering (CMTF) reduces tool-choice confusion by exposing only the next causally necessary tool frontier, but it assumes that the user request has already been mapped to a symbolic goal state. In practice, requests such as "handle my appointment" or "take care of this email" may correspond to multiple possible goals. This creates wrong-goal execution, where an agent follows a valid causal tool path for an unintended objec
The paper addresses a critical challenge in current LLM agents, which are increasingly deployed in real-world scenarios requiring robust tool integration and goal interpretation.
Improving goal-state inference in LLM agents is crucial for their reliability and effectiveness, preventing erroneous actions and enhancing user trust in autonomous systems.
This research streamlines the interaction between LLM agents and external tools by tackling ambiguity in user requests, leading to more precise and efficient agent execution.
- · AI Agent developers
- · Enterprises deploying LLM agents
- · AI infrastructure providers
- · LLM agents with poor goal-state inference
Increased reliability and robustness of tool-augmented LLM agents for complex tasks.
Accelerated adoption of AI agents in critical business and personal applications, collapsing certain workflow layers.
Further commoditization of basic AI agent capabilities as more sophisticated and reliable systems emerge.
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Read at arXiv cs.AI