---
license: cc-by-4.0
language:
- en
pretty_name: AURA Implicit-Intent Benchmark (AURATown)
annotations_creators:
- expert-generated
source_datasets:
- original
task_categories:
- question-answering
- text-classification
tags:
- theory-of-mind
- implicit-intent
- social-intelligence
- situated-agents
- llm-agents
- tool-use
- proactive-probing
size_categories:
- n<1K
configs:
- config_name: implicit_intent
  default: true
  data_files:
  - split: test
    path: implicit_intent.jsonl
- config_name: implicit_intent_v1
  data_files:
  - split: test
    path: implicit_intent_v1.jsonl
- config_name: scenes
  data_files:
  - split: test
    path: scenes.jsonl
- config_name: privacy_distractor
  data_files:
  - split: test
    path: privacy_distractor.jsonl
- config_name: factual_grounding
  data_files:
  - split: test
    path: factual_grounding.jsonl
- config_name: grounding_templates
  data_files:
  - split: test
    path: grounding_templates.jsonl
---

# AURA Implicit-Intent Benchmark (AURATown)

A small, **author-authored** evaluation suite for studying **implicit-need
surfacing** by situated LLM agents. 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 *available*, *in a good mood*, or *worth interrupting now*.
This benchmark separates the **literal** answer (readable from public scene
state) from the **implicit** need (which requires private/hidden state),
and labels which tools are *required* vs *forbidden* to answer it.

All queries are grounded in **AURATown**, a small grid-based social simulation
with 5 named agents (Lin Wei, Zhang Hao, Chen Mei, Liu Yang, Wang Jun) and a set
of named locations. Each scene fixes a time of day, locations, and per-agent
public/private state so the benchmark tests cross-scene robustness rather than
memorisation of one configuration.

> **Paper:** *AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated
> LLM Agents* — Li, Liu, Cai, Xu (2026). arXiv:[2606.05557](https://arxiv.org/abs/2606.05557).

## Configs

| Config | Rows | What it is |
|---|---:|---|
| `implicit_intent` *(default)* | 100 | Primary benchmark: 4 scenes × 25 queries, 5 subcategories. |
| `implicit_intent_v1` | 25 | Pilot subset (= Scene A of v2, single scene). Used for the IAA study. |
| `scenes` | 4 | Scene metadata: public/private state + beliefs (nested state JSON-encoded). |
| `privacy_distractor` | 30 | Factual questions that must **not** touch private/historical state; each carries `allowed_tools` / `forbidden_tools`. |
| `factual_grounding` | 50 | Plain environment-grounded factual queries (spatial/social/temporal/memory/planning). |
| `grounding_templates` | 22 | Parameterised templates that generate verifiable questions from simulation state. |

The original, un-flattened JSON files are also shipped verbatim under `raw/`.

## Fields (`implicit_intent` config)

| Field | Type | Description |
|---|---|---|
| `id` | int | Stable query id. |
| `scene` | str | Scene key, e.g. `A_cafe_morning`. |
| `scene_summary` | str | One-line natural-language scene context. |
| `subcategory` | str | One of `availability`, `mood`, `appropriateness`, `latent_goal`, `second_order`. |
| `agent_subject` | str | Agent the query is about. |
| `target` | str / null | For `second_order` queries, the third party being reasoned about. |
| `query` | str | The surface user query. |
| `literal_requires` | list[str] | Public state needed for the literal answer. |
| `implicit_requires` | list[str] | Private state needed for the implicit answer. |
| `implicit_need` | str | One-line statement of what the user is *really* asking. |
| `gold_required_tools` | list[str] | Tools an oracle must call to surface the implicit need. |
| `forbidden_tools` | list[str] | Tools that would over-reach / leak private state. |

## Construction & annotation

- Queries were **hand-authored** by the paper authors. Scene A reuses the 25
  pilot queries verbatim; scenes B/C/D are new and authored to keep surface
  forms disjoint from the pilot. Stale-belief templates are adapted from
  Ullman (2023).
- **Inter-annotator agreement**: two independent annotators relabelled the
  pilot 25 queries' subcategory under the same 5-class scheme; Cohen's
  κ = 0.61 (substantial, Landis–Koch), with disagreements concentrated on the
  mood/appropriateness/availability boundary. The author labels are retained as
  gold. (IAA raw response files are *not* included in this release.)

## Intended use & scope

Designed to evaluate whether an agent **surfaces the implicit need** behind a
situated query while respecting tool/privacy boundaries — **not** general QA.
On purely factual lookup the implicit-intent machinery is *not* expected to help
(see `factual_grounding` / `privacy_distractor`, which are control slices).

## Limitations

- **Small** (100 primary queries) and **single-simulator** (AURATown only); the
  agent roster and scene set are fixed.
- Author-written gold labels; the IAA (κ=0.61) is supportive but the sample is
  small. Treat subcategory labels as a useful partition, not ground truth.
- English only; agent names are romanised Mandarin but all query text is English.

## Related external benchmarks

This suite is *original*; cross-domain checks in the paper use the external
benchmarks **FANToM** (Kim et al., 2023), **LoCoMo** (Maharana et al., 2024),
**GAIA**, and **SOTOPIA** (Zhou et al., 2024). Those datasets are **not**
redistributed here — obtain them from their original sources.

## Citation

```bibtex
@misc{li2026aura,
  title         = {AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents},
  author        = {Li, Yang and Liu, Jiaxiang and Cai, Jiang and Xu, Mingkun},
  year          = {2026},
  eprint        = {2606.05557},
  archivePrefix = {arXiv},
  url           = {https://arxiv.org/abs/2606.05557}
}
```

## License

CC-BY-4.0. You may share and adapt with attribution.