arXiv:2606.03135v1 Announce Type: new Abstract: Large Language Model (LLM) agents often operate under underspecified user instructions, where latent uncertainty over user intent leads to erroneous tool actions. To address this challenge, we propose a goal-oriented clarification framework that aligns clarification behavior with ambiguity resolution. Central to our approach is the Information Gain Reward, a metric that quantifies the utility of clarification questions by measuring the Bayesian belief update towards the ground-truth goal induced by the clarification exchange. We train the clarifi
The rapid deployment and increasing sophistication of Large Language Models necessitate solutions for their inherent challenges with underspecified instructions, making robust clarification frameworks critical for practical applications.
This development directly addresses a core limitation of LLM agents, enhancing their reliability and autonomy which is crucial for their integration into complex workflows and decision-making processes.
LLM agents can now more effectively resolve user intent ambiguity through goal-oriented clarification, leading to more accurate and reliable task execution.
- · AI developers
- · Enterprises adopting AI agents
- · SaaS providers integrating LLMs
- · Companies with inefficient, human-supervised workflows for LLMs
More robust and reliable AI agents become deployable in business operations.
Increased trust in AI agent capabilities could accelerate automation across white-collar sectors.
A competitive advantage emerges for companies effectively deploying AI agents that autonomously manage complex tasks.
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Read at arXiv cs.AI