Uncertainty-Aware Clarification in LLM Agents with Information Gain

In three linesFramework for LLM agents operating under underspecified instructions. Proposes Information Gain Reward metric quantifying clarification utility via Bayesian belief updates. Validation on τ-Bench: +3.7% success rate vs no-clarification baseline, +0.3 interaction steps.

Summary generated by Claude — human-verified