Datasets:

limberc
/

this-that-spatial-bench

---
license: mit
task_categories:
- question-answering
- multiple-choice
language:
- en
tags:
- decision-making
- calibration
- spatial-reasoning
- synthetic
- computed-ground-truth
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: test
    path: test.jsonl
---
# spatial-decisions

7,305 multiple-choice decision questions over 6,525 distinct simulated states, in
15 families and two environments. **Every answer is computed from the simulator**, not
annotated by a person and not taken from a model. That is the point of the set: on a question whose
answer is derived from the rules of the environment, a disagreement is a mistake, and there is
nothing to argue about.

The set was built to replace a much narrower public artefact: a recording of 68 questions of a
single shape -- "can the agent step one cell north?" -- over 17 maze windows. That recording is
useful and tiny. This one keeps the computed-truth property and adds question types, environments,
renderings, option counts and answer types.

## What is in it

| field | meaning |
|---|---|
| `id` | stable identifier, `family-index` |
| `family` | which generator produced the item |
| `environment` | `maze` or `snake` |
| `state` | the rendered state shown to the system |
| `question` | the question text |
| `options` | the answer options, in canonical order |
| `answer_index` | index into `options` |
| `answer` | the option text itself |
| `answer_distribution` | the true distribution over `options`. One-hot for the deterministic families; for `stochastic` it is the probability the environment's transition rules define |
| `deterministic` | `false` only for `stochastic` |
| `seen_in_training` | whether a question of this shape appears in the authors' training mixture |
| `fingerprint` | sha256 over the rendered state, question and sorted options |

## Families

| family | env | n | options | majority label | question |
|---|---|---|---|---|---|
| `move_legal` | maze | 500 | 2 | 0.50 | Can the agent step one cell in a named direction? |
| `move_two_step` | maze | 500 | 2 | 0.50 | Are both steps of a two-move plan legal? |
| `open_count` | maze | 400 | 5 | 0.25 | How many of the four orthogonal neighbours are traversable? |
| `cell_kind` | maze | 498 | 3 | 0.33 | Is the agent on a dead end, a corridor, or a junction? |
| `only_way` | maze | 415 | 6 | 0.20 | Which single direction is open, if exactly one is? |
| `blocked_pair` | maze | 500 | 2 | 0.50 | Are two named directions both blocked? (a conjunction) |
| `onward` | maze | 500 | 2 | 0.50 | After a named move, is a further named move legal from the new cell? |
| `plan_survives` | maze | 500 | 4 | 0.25 | Does a three-step plan complete, and if not at which step does it fail? |
| `goal_bearing` | maze | 496 | 8 | 0.12 | In which of eight compass directions does the goal lie? |
| `reachable_within` | maze | 500 | 2 | 0.50 | Is the goal reachable within k moves? |
| `distance_band` | maze | 500 | 5 | 0.20 | Which band does the shortest-path distance fall in? |
| `first_move` | maze | 500 | 4 | 0.25 | Which single first move starts a shortest path? |
| `stochastic` | maze | 500 | 2 | 0.50 | Under a noisy actuator of stated reliability, will the executed move be safe? |
| `snake_safe` | snake | 500 | 2 | 0.50 | Does the snake survive the next move in a named direction? |
| `snake_food` | snake | 496 | 8 | 0.12 | In which of eight compass directions does the food lie? |

## Properties worth knowing before you use it

**Every family is exactly label-balanced by construction.** Items are bucketed by *answer* and the
same number is drawn from each bucket, so the majority-label column above equals one over the option
count in every row. A family whose rare answer is genuinely rare yields fewer questions rather than
being topped up from the common one. A constant answer scores chance, everywhere.

**One family has a real probability, not a label.** In `stochastic` the actuator executes the
intended move with probability rho and otherwise picks uniformly among the others, so the
probability that the executed move is safe follows from the transition rules. `answer_index` is a
single sampled outcome; `answer_distribution` is the analytic probability. Scoring accuracy against
the sample has a ceiling below 1 by construction; scoring distance to `answer_distribution` does
not, and is the more informative measurement.

**2,000 of 7,305 items are in families the authors' own models were trained on**, marked by
`seen_in_training`. Results should be reported for the two halves separately. A specialist model
beating a general one on a question shape it was trained for is a real product fact and a weak
scientific one; they are different claims and averaging them hides both.

**Contamination.** Every item was checked by fingerprint against the authors' training mixtures and
any verbatim match was discarded. The check is over the rendered state, question and option set.

## Limitations

- Synthetic. It measures spatial and procedural decision-making in two toy environments, and
  nothing about natural language, world knowledge or open-ended reasoning.
- The `maze` and `snake` simulators are small. A system that has seen a lot of grid puzzles may
  find the deterministic families easy; the `stochastic` family is the one that stays hard, because
  it asks for a number rather than a fact.
- The English is templated, with two or three phrasings per family. It is not a test of
  robustness to natural phrasing.

## Provenance and licence

The maze and snake simulators, and the noisy-actuator construction, derive from the open
[NanoJev](https://huggingface.co/C-Tianyu/NanoJev) project under the MIT licence; they are used as
published. The generators, the balancing, the contamination check and this dataset are released
under the MIT licence as well.

	---
	license: mit
	task_categories:
	- question-answering
	- multiple-choice
	language:
	- en
	tags:
	- decision-making
	- calibration
	- spatial-reasoning
	- synthetic
	- computed-ground-truth
	size_categories:
	- 1K<n<10K
	configs:
	- config_name: default
	data_files:
	- split: test
	path: test.jsonl
	---

	# spatial-decisions

	7,305 multiple-choice decision questions over 6,525 distinct simulated states, in
	15 families and two environments. Every answer is computed from the simulator, not
	annotated by a person and not taken from a model. That is the point of the set: on a question whose
	answer is derived from the rules of the environment, a disagreement is a mistake, and there is
	nothing to argue about.

	The set was built to replace a much narrower public artefact: a recording of 68 questions of a
	single shape -- "can the agent step one cell north?" -- over 17 maze windows. That recording is
	useful and tiny. This one keeps the computed-truth property and adds question types, environments,
	renderings, option counts and answer types.

	## What is in it

	\| field \| meaning \|
	\|---\|---\|
	\| `id` \| stable identifier, `family-index` \|
	\| `family` \| which generator produced the item \|
	\| `environment` \| `maze` or `snake` \|
	\| `state` \| the rendered state shown to the system \|
	\| `question` \| the question text \|
	\| `options` \| the answer options, in canonical order \|
	\| `answer_index` \| index into `options` \|
	\| `answer` \| the option text itself \|
	\| `answer_distribution` \| the true distribution over `options`. One-hot for the deterministic families; for `stochastic` it is the probability the environment's transition rules define \|
	\| `deterministic` \| `false` only for `stochastic` \|
	\| `seen_in_training` \| whether a question of this shape appears in the authors' training mixture \|
	\| `fingerprint` \| sha256 over the rendered state, question and sorted options \|

	## Families

	\| family \| env \| n \| options \| majority label \| question \|
	\|---\|---\|---\|---\|---\|---\|
	\| `move_legal` \| maze \| 500 \| 2 \| 0.50 \| Can the agent step one cell in a named direction? \|
	\| `move_two_step` \| maze \| 500 \| 2 \| 0.50 \| Are both steps of a two-move plan legal? \|
	\| `open_count` \| maze \| 400 \| 5 \| 0.25 \| How many of the four orthogonal neighbours are traversable? \|
	\| `cell_kind` \| maze \| 498 \| 3 \| 0.33 \| Is the agent on a dead end, a corridor, or a junction? \|
	\| `only_way` \| maze \| 415 \| 6 \| 0.20 \| Which single direction is open, if exactly one is? \|
	\| `blocked_pair` \| maze \| 500 \| 2 \| 0.50 \| Are two named directions both blocked? (a conjunction) \|
	\| `onward` \| maze \| 500 \| 2 \| 0.50 \| After a named move, is a further named move legal from the new cell? \|
	\| `plan_survives` \| maze \| 500 \| 4 \| 0.25 \| Does a three-step plan complete, and if not at which step does it fail? \|
	\| `goal_bearing` \| maze \| 496 \| 8 \| 0.12 \| In which of eight compass directions does the goal lie? \|
	\| `reachable_within` \| maze \| 500 \| 2 \| 0.50 \| Is the goal reachable within k moves? \|
	\| `distance_band` \| maze \| 500 \| 5 \| 0.20 \| Which band does the shortest-path distance fall in? \|
	\| `first_move` \| maze \| 500 \| 4 \| 0.25 \| Which single first move starts a shortest path? \|
	\| `stochastic` \| maze \| 500 \| 2 \| 0.50 \| Under a noisy actuator of stated reliability, will the executed move be safe? \|
	\| `snake_safe` \| snake \| 500 \| 2 \| 0.50 \| Does the snake survive the next move in a named direction? \|
	\| `snake_food` \| snake \| 496 \| 8 \| 0.12 \| In which of eight compass directions does the food lie? \|

	## Properties worth knowing before you use it

	Every family is exactly label-balanced by construction. Items are bucketed by answer and the
	same number is drawn from each bucket, so the majority-label column above equals one over the option
	count in every row. A family whose rare answer is genuinely rare yields fewer questions rather than
	being topped up from the common one. A constant answer scores chance, everywhere.

	One family has a real probability, not a label. In `stochastic` the actuator executes the
	intended move with probability rho and otherwise picks uniformly among the others, so the
	probability that the executed move is safe follows from the transition rules. `answer_index` is a
	single sampled outcome; `answer_distribution` is the analytic probability. Scoring accuracy against
	the sample has a ceiling below 1 by construction; scoring distance to `answer_distribution` does
	not, and is the more informative measurement.

	2,000 of 7,305 items are in families the authors' own models were trained on, marked by
	`seen_in_training`. Results should be reported for the two halves separately. A specialist model
	beating a general one on a question shape it was trained for is a real product fact and a weak
	scientific one; they are different claims and averaging them hides both.

	Contamination. Every item was checked by fingerprint against the authors' training mixtures and
	any verbatim match was discarded. The check is over the rendered state, question and option set.

	## Limitations

	- Synthetic. It measures spatial and procedural decision-making in two toy environments, and
	nothing about natural language, world knowledge or open-ended reasoning.
	- The `maze` and `snake` simulators are small. A system that has seen a lot of grid puzzles may
	find the deterministic families easy; the `stochastic` family is the one that stays hard, because
	it asks for a number rather than a fact.
	- The English is templated, with two or three phrasings per family. It is not a test of
	robustness to natural phrasing.

	## Provenance and licence

	The maze and snake simulators, and the noisy-actuator construction, derive from the open
	[NanoJev](https://huggingface.co/C-Tianyu/NanoJev) project under the MIT licence; they are used as
	published. The generators, the balancing, the contamination check and this dataset are released
	under the MIT licence as well.