arXiv:2606.19559v1 Announce Type: cross Abstract: Recent position papers argue that the classical aleatoric/epistemic uncertainty framework is insufficient for interactive large language model (LLM) agents and call for underspecification-aware, decomposed, and communicable uncertainty representations that can unlock new agent capabilities such as proactive clarification seeking and shared mental-model building. Practical deployment constraints -- black-box APIs, interactive latency budgets, and the absence of labeled trajectories -- rule out logprob-based, multi-sampling, and training-based me
The rapid advancement and deployment of large language models are exposing their limitations in interactive, real-world agentic applications, necessitating better uncertainty management for practical deployment.
Improved uncertainty decomposition and clarification seeking in LLM agents will enhance their reliability, autonomy, and ability to handle complex tasks, accelerating their integration into automated workflows.
This research suggests a move towards LLM agents that can proactively identify and communicate their uncertainties, leading to more robust and trustworthy autonomous systems.
- · AI developers
- · Enterprises deploying AI agents
- · Robotics
- · Developers relying on black-box LLM deployments
- · Systems with high ambiguity tolerance
LLM agents become more capable of complex decision-making and interaction without human intervention.
Increased trust and adoption of AI agents across various industries, leading to deeper automation of white-collar tasks.
The development of new human-AI interaction paradigms centered around uncertainty communication and collaborative problem-solving.
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Read at arXiv cs.CL