# From Questions to Decisions: Benchmarking How LLM Agents Acquire Decision-Relevant Information From Human Beings

> **Platform:** [SPIDITS AI](https://spidits.com/) — Real-Time AI News & Market Intelligence  
> **Published:** 2026-11-11T05:00:00.000Z  
> **Category:** ACQUISITION  
> **Impact Score:** 250/100  
> **Primary Source:** [arXiv AI](https://arxiv.org/abs/2611.13015)  
> **Canonical Citation:** [https://spidits.com/timeline/from-questions-to-decisions-benchmarking-how-llm-agents-acquire-decision](https://spidits.com/timeline/from-questions-to-decisions-benchmarking-how-llm-agents-acquire-decision)

## Executive Summary
LLM agents are increasingly asked to make plans for people, such as scheduling drivers or assigning nurses.

## Why It Matters (Strategic Analysis)
Incomplete human inputs directly degrade the performance of autonomous planning agents deployed in complex operational environments. Standardizing how agents elicit missing contextual variables provides a critical blueprint for building robust, interactive decision-making systems in enterprise workflow automation.

## Referenced Coverage & Sources
- **[arXiv AI](https://arxiv.org/abs/2611.13015)**: From Questions to Decisions: Benchmarking How LLM Agents Acquire Decision-Relevant Information from Human Beings — _LLM agents are increasingly asked to make plans for people, such as scheduling drivers or assigning nurses. However, people's requests are often incomplete..._

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*Synthesized by SPIDITS AI Market Intelligence Desk. Track live AI news, model releases, and funding: [https://spidits.com](https://spidits.com)*