Uncertainty Quantification for LLM Function-Calling
Factual evidence
What the source reports
Research explores methods for quantifying uncertainty in Large Language Model (LLM) function calls to prevent incorrect, irreversible actions.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 4 November 2026
- Collected by OneBench
- 28 Apr 2026, 21:40 UK
- Original headline
- Uncertainty Quantification for LLM Function-Calling ↗
Stored source excerpt
arXiv:2604.22985v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed to autonomously solve real-world tasks. A key ingredient for this is the LLM…
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OneBench interpretation
Institutional assessment
So what
Robust uncertainty quantification for LLM function calling is critical for safe deployment of autonomous AI agents in banking, directly addressing a core model risk for irreversible actions.
Do what
This research provides a necessary framework for evaluating the safety and reliability of agentic AI systems integrating with core banking functions.