AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents
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Computer Science > Computation and Language
Title:AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents
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 this https URL.
| Comments: | Submitted to EMNLP 2026. Code, simulator, and benchmark: this https URL |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7; I.2.11 |
| Cite as: | arXiv:2606.05557 [cs.CL] |
| (or arXiv:2606.05557v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.05557
arXiv-issued DOI via DataCite (pending registration)
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