AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents

2026-06-03

AI Summary

This work addresses the limitation of existing embodied language model agents, which respond only to literal queries and fail to capture users’ implicit intentions—such as inferring availability or emotional state from a question like “Where is Lin Wei?” To bridge this gap, the authors propose IntentFrame, a framework that introduces structured intent reasoning between scene perception and tool invocation to explicitly model latent user needs. IntentFrame incorporates a gap-calibration mechanism that dynamically allocates exploration budgets and selects appropriate tools. This approach enables the first targeted probing for implicit intents without relying on memorized answers. Evaluated on a benchmark of 100 queries across four scenarios, IntentFrame improves implicit intent coverage by 0.07 over ReAct (p < 10⁻⁶), reduces probing actions by 82%, and entirely eliminates privacy-violating tool calls.

Abstract

A situated query like "where is Lin Wei?" often encodes more than its literal content: the user may also want to know whether Lin Wei is free, in a good mood, or worth interrupting now. Standard tool-use agents answer the literal question and stop. AURA inserts an inference step between scene perception and tool use that produces an IntentFrame: a structured estimate of the implicit need with a scalar gap score that controls per-query probe budget and tool selection. On a 100-query four-scene implicit-intent benchmark, AURA improves implicit-need coverage over ReAct-style probing (Delta = +0.07, p < 10^-6); three of four scenes are individually significant, the gain reproduces on a second backbone, and a prompt ablation attributes the lift to gap calibration rather than answer memorisation. On factual lookup the controller trades raw accuracy for 82% fewer probes and zero forbidden-tool violations on a privacy-sensitive slice; scope conditions are detailed in Limitations. Code, simulator, and benchmark are released at https://github.com/innovation64/AURA.

Problem

• implicit need
• situated agents
• intent understanding
• tool-use
• query probing

Innovation

• IntentFrame
• implicit-need surfacing
• gap score
• situated LLM agents
• adaptive probing