arXiv:2607.07626v1 Announce Type: new Abstract: Reliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as retrieval, tool use, and adaptive computation depend on accurately estimating answer reliability. Existing approaches, however, largely treat confidence as a property of completed responses, overlooking how confidence-related information evolves throughout the answering process. In this work, we investigate confidence from a temporal perspective by comparing pre-solution Feeling-of-Knowing (FOK) an
The increasing deployment of large language models in critical applications necessitates robust confidence estimation for reliable and safe operation.
Accurate and temporal confidence estimation in LLMs is crucial for developing reliable AI agents and systems that can adapt and make decisions effectively.
Confidence is no longer just an outcome; it's an evolving property within LLMs that can be monitored and utilized during the generation process.
- · AI developers
- · High-stakes AI applications
- · Developers of AI agents
- · Companies with unreliable AI systems
- · Simple LLM confidence metrics
More reliable and trustworthy LLM-powered applications emerge, especially in critical domains.
This improved reliability accelerates the adoption and integration of AI agents across various industries.
The enhanced temporal understanding of LLM confidence could lead to new architectures for self-improving and robust AI systems.
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