innovation64/aura-implicit-intent

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.