arXiv:2606.06976v1 Announce Type: new Abstract: Large language model (LLM)-based agents often make suboptimal tool-use decisions, including unsupported tool invocation and hallucinated direct responses, which may accumulate errors throughout multi-step interactions. Existing approaches mainly improve these behaviors through inference-time correction or coarse-grained reward signals based on decision outcomes and structured checklists, leaving the uncertainty characteristics of agent decisions underexplored. We observe that decision-oriented reinforcement learning tends to weaken the uncertaint
The proliferation of LLM-based agents makes the problem of suboptimal tool-use decisions an immediate critical challenge for their real-world deployment.
Improved agentic decision-making, particularly in tool-use, is fundamental for advancing autonomous AI systems beyond narrow applications and into complex, multi-step interactions.
This research provides a new pathway to build more robust and reliable AI agents by addressing their decision uncertainty, potentially reducing errors and hallucinations.
- · AI Agent Developers
- · Enterprises Adopting AI Agents
- · Cloud AI Providers
- · Inefficient LLM-based Agent Architectures
- · Manual Workflow Providers
- · Companies with high error tolerance in automation
More reliable and less error-prone AI agents can be deployed in sensitive applications.
Increased trust in autonomous agents accelerates their adoption across various industries, leading to greater automation of white-collar tasks.
The enhanced decision-making capabilities of AI agents could lead to unprecedented levels of productivity and further reshape the future of work.
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Read at arXiv cs.AI