Datasets:

innovation64
/

aura-implicit-intent

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.

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

@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.