Datasets:

StanfordNeuroAILab
/

FlowBench

---
pretty_name: FlowBench
license: other # TODO: set the release licence; source frames keep their original licences (see "Licences")
task_categories:
- visual-question-answering
- image-text-to-text
language:
- en
tags:
- spatial-reasoning
- counterfactual-reasoning
- physical-reasoning
- embodied-ai
- robotics
- set-of-marks
- multiple-choice
- ai2thor
size_categories:
- 100K<n<1M
configs:
- config_name: sim_colour_som
  default: true
  data_files:
  - split: train
    path: sim/parquet/colour_som/train-*.parquet
  - split: test
    path: sim/parquet/colour_som/test-*.parquet
- config_name: sim_red_som
  data_files:
  - split: train
    path: sim/parquet/red_som/train-*.parquet
  - split: test
    path: sim/parquet/red_som/test-*.parquet
- config_name: sim_natural
  data_files:
  - split: train
    path: sim/parquet/natural/train-*.parquet
  - split: test
    path: sim/parquet/natural/test-*.parquet
- config_name: real_colour_som
  data_files:
  - split: train
    path: real/parquet/colour_som/train-*.parquet
  - split: test
    path: real/parquet/colour_som/test-*.parquet
- config_name: real_red_som
  data_files:
  - split: train
    path: real/parquet/red_som/train-*.parquet
  - split: test
    path: real/parquet/red_som/test-*.parquet
---
# FlowBench

**FlowBench** is a benchmark and training set for **counterfactual spatial reasoning** in vision-language models.
Each question shows a scene, says how one object will move (for example "0.60 m to the right" or "about 8 cm up"),
and asks about the scene **after** the move: which object will it be closest to, will it end up left of a landmark,
will it hide another object, will it hit a wall. To answer, a model has to imagine how the scene changes, not
just read the image.

Every question comes with the recorded **after** image, so the same question can also be asked as a plain
perception question on the outcome. Answers are computed from geometry: simulator state for FlowBench-Sim and
measured 3D geometry for FlowBench-Real. No model chooses the answers.

| subset | scenes | split | questions | image pairs | image styles |
|---|---|---|---|---|---|
| **FlowBench-Sim** | AI2-THOR indoor houses | `train` (ProcTHOR-10k) | 419,612 | 117,604 scene samples | `natural`, `colour_som`, `red_som` |
| | | `test` (iTHOR floor plans) | 714 | 226 scene samples | |
| **FlowBench-Real** | real robot-manipulation episodes | `train` | 8,575 | 5,367 | `colour_som`, `red_som` |
| | (Bridge V2, DROID, Language-Table, RT-1, Berkeley UR5) | `test` | 1,083 | 1,083 | |

In both subsets, train and test come from disjoint scenes: different houses in Sim, different image pairs in Real.

## Contents

- [Task](#task)
- [Configurations and loading](#configurations-and-loading)
- [Question categories](#question-categories)
- [Image styles](#image-styles)
- [FlowBench-Sim](#flowbench-sim)
- [FlowBench-Real](#flowbench-real)
- [Evaluation notes](#evaluation-notes)
- [Limitations](#limitations)
- [Licences](#licences)
- [Citation](#citation)

## Task

Each row has a **before** image `image_before`, an **after** image `image_after`, and two versions of the question:

| field | shown with | what the model does | example (Sim, `colour_som`) |
|---|---|---|---|
| `motion_command` + `question` | `image_before` | **counterfactual**: predict what happens after the stated move | `move the kettle (marked orange) 0.60m right` + "After the move, which three objects are closest to the kettle (marked orange)?" |
| `static_question` | `image_after` | **static**: read the outcome off the after image | "Which three objects are closest to the kettle (marked orange)?" |

Both versions share the options `choice_A` … `choice_F` and the answer `answer`. The gap between static and
counterfactual accuracy shows how much a model loses when it has to imagine the outcome instead of seeing it.

A FlowBench-Real example (`colour_som`, RT-1):

> *For scale, the object marked yellow is about 6 cm across at its widest point. If the robot arm moves the object
> marked blue so that, from the camera's perspective, its center ends up about 14 cm to the left, and about 19 cm
> down from where it was before (directions as seen from the camera), which of these will the object marked blue end
> up closest to, by straight-line distance?*
> A. The object marked yellow. B. The object marked red. C. The object marked cyan.

## Configurations and loading

| config | subset | image style | train | test | parquet size |
|---|---|---|---|---|---|
| `sim_colour_som` (default) | Sim | coloured marks | 419,612 | 714 | ~62 GB |
| `sim_red_som` | Sim | red labelled badges | 419,612 | 714 | ~61 GB |
| `sim_natural` | Sim | clean renders | 419,612 | 714 | ~46 GB |
| `real_colour_som` | Real | coloured marks | 8,575 | 1,083 | ~1.2 GB |
| `real_red_som` | Real | red numbered badges | 8,575 | 1,083 | ~1.0 GB |

Within a subset, all styles share question ids (`id`). In Sim they also share the option order and answer
letter, except for some `natural` rows (see `choices_match_som`). In Real they share the options and answer letter.

```python
from datasets import load_dataset
# test sets are small
sim_test = load_dataset("StanfordNeuroAILab/FlowBench", "sim_colour_som", split="test")
real_test = load_dataset("StanfordNeuroAILab/FlowBench", "real_colour_som", split="test")
row = sim_test[0]
row["image_before"], row["image_after"]  # PIL images
row["motion_command"], row["question"], row["answer"]
# Sim train is large; stream it
sim_train = load_dataset("StanfordNeuroAILab/FlowBench", "sim_colour_som", split="train", streaming=True)
```

Options are stored in separate columns, and unused letters are `null`. A simple way to build a prompt for Sim:

```python
letters = [l for l in "ABCDEF" if row[f"choice_{l}"] is not None]
options = "\n".join(f"{l}. {row[f'choice_{l}']}" for l in letters)
counterfactual_prompt = f"{row['motion_command']}\n{row['question']}\n{options}"  # with image_before
static_prompt = f"{row['static_question']}\n{options}"                           # with image_after
```

FlowBench-Real rows also include the exact text used in our evaluations: `prompt` for the counterfactual
question and `static_prompt` for the static one.

## Question categories

| category | question (about the scene after the move) | Sim train | Sim test | Real train | Real test |
|---|---|---|---|---|---|
| `closer_to` | Which of several objects will the moved object be closest to? | 59,474 | 94 | 209 | 189 |
| `closest_three` | Which three of five or six objects will be closest to it? (3-letter answer) | 66,679 | 112 | 81 | 135 |
| `count_near` | How many of the listed objects will be within a given distance of it? | 65,908 | 110 | 317 | 131 |
| `left_right_landmark` (Sim) / `left_right` (Real) | Will it be left or right of a landmark, as seen from the camera? | 67,554 | 116 | 448 | 193 |
| `near_object` | Will it be within a given distance of another object? | 67,636 | 117 | 6,831 | 193 |
| `occlusion` | Will a landmark be fully visible, or partly or fully hidden by it? | 13,576 | 40 | 20 | 96 |
| `feasibility` | Starting from the after state, can it make a further stated move without colliding? | 45,869 | 106 | – | – |
| `contact_wall` | Will it be touching a wall? | 32,916 | 19 | – | – |
| `depth_order` | Will it be closer to the camera than a landmark? (Bridge V2 and DROID only) | – | – | 669 | 146 |
| **total** | | **419,612** | **714** | **8,575** | **1,083** |

`mcq_type` gives the format: `mcq2` (yes/no), `mcq3`, `mcq4`, `mcq5_select3` or `mcq6_select3`. For `count_near`,
the options are counts.

## Image styles

To refer to specific objects, most styles draw marks on the image (Set-of-Marks, SoM):

| style | image | how the text refers to objects |
|---|---|---|
| `natural` (Sim only) | clean render | names and positions: "the 2nd kettle from the left" |
| `colour_som` | solid coloured dots with a white rim and no text. Colours are sampled for each question; in Real, a dot never has the colour of the object under it | Sim: "the kettle (marked blue)"; Real: "the object marked orange" |
| `red_som` | red badges with a label | Sim: mover `1`, landmark or occludee `2`, option objects `A`–`F` ("the kettle (1)", "(A) the plate"). Real: numbers on every marked object ("the object marked 3") |

In the after image, marks are drawn where the objects ended up. In FlowBench-Real, the 103 test and 681 train
**named-object** questions (Bridge V2 and DROID, `named_objects = true`) have no marks. They refer to objects by
name ("the leftmost soup can") and appear in both Real styles with clean frames.

---

## FlowBench-Sim

### Source and generation

Scenes come from AI2-THOR. **Train** uses ProcTHOR-10k houses and **test** uses the hand-built iTHOR floor plans,
so no test house appears in training. In each *scene sample* (`sample_id`), one object (`mover_object_id`) is moved
by a stated distance and direction in the camera frame ("left", "right", "forward along the camera axis", ...).
The simulator renders the scene before and after the move. Each category has its own view (`view_id` =
`view_<category>`, camera plus move) and one question per view. The answer is computed from simulator metadata:
bounding-box distances, screen positions, occlusion cover, collision-checked probes and wall clearances.

Filters on the released rows:
- ground truth is complete;
- the mover is not a structural object (windows, doors, cabinets, drawers, sinks, faucets, countertops);
- every referenced mark is drawn;
- for `occlusion`, the occludee covers at least 150 px, and any cover by the mover counts as occluded;
- static questions never mention the move;
- answer letters are balanced within each category. Yes/no questions are balanced within each polarity, so a
  letter never stands for "yes".

### How the three styles relate

- **`colour_som`** is the reference version of every question.
- **`red_som`** asks the same questions with the same choice permutation. Mapping each red mark id to its colour
  gives the `colour_som` row exactly, which was checked for every row. In 66,601 train and 205 test rows the
  permutation moves lettered option objects, so the red badges were redrawn with the new letters
  (`som_relabelled`).
- **`natural`** comes from the unmarked export of the same scene samples, with the same text fixes. When it asks
  about the same objects as the SoM rows, its options follow the SoM order (309,142 of 419,612 train rows and 510
  of 714 test rows; `choices_match_som = true`). The other rows, which are most `closer_to` rows and all
  `count_near` rows, use different distractors or object lists. They are separate questions with their own
  balanced letters.

### Reasoning annotations (`cot_gt`, `cot_1`, `cot_0`)
Sim rows include up to three chains of thought, each ending with the answer. They can be used for supervised
reasoning training.
| field | written by | inputs |
|---|---|---|
| `cot_gt` | deterministic rules for each category | simulator metadata before and after the move (distances, screen positions, cover rates, collision checks, wall clearances) |
| `cot_1` | Qwen3-VL-32B-Instruct (most categories); Qwen3.5-9B (`feasibility`, `contact_wall`) | question, before **and** after images, `cot_gt` |
| `cot_0` | Qwen3.5-9B (thinking off) | question, motion command and the **before** image only, with `cot_1` as a private hint |

- **`cot_gt`** is written separately for each style, in that row's wording and options. It states the rule that
  produced the category's ground truth, the recorded numbers and the conclusion, and the conclusion matches the
  row's answer. It is present on 417,748 / 419,612 train rows and 711 / 714 test rows. It is `null` on 1,811
  train and 3 test `closest_three` rows whose stored answer disagrees with their own after image; those answers
  are probably wrong. It is also `null` on 53 `natural` rows that list two options with the same text.
- **`cot_1` / `cot_0`** are train-only. They were written for the natural question and appear on 278,662 `natural`
  rows and on 248,777 rows each in `colour_som` and `red_som`, wherever the SoM question asks about the same
  objects. Each `cot_0` passed automatic checks: its final answer equals `cot_1`'s; it does not mention an after or
  second image or any hint; it never states a direction that contradicts the motion; its word overlap with
  `cot_1` is at most 0.8; and it is 25–260 words long. Because they are VLM-written, these texts can get scene
  details wrong when those details do not affect the answer.

### Sim row fields

| field | description |
|---|---|
| `id` | `<sample_id>__<question_id>`, identical across the three styles |
| `sample_id`, `scene`, `view_id`, `question_id` | scene sample, AI2-THOR scene, view (camera and move), question |
| `category`, `mcq_type`, `n_choices` | question category, format, number of options |
| `image_before`, `image_after` | before and after images (800×600) |
| `motion_command` | the move, e.g. "move the bowl (marked blue) 0.50m left" |
| `question`, `static_question` | counterfactual and static question text |
| `choice_A` … `choice_F`, `answer` | options (`null` if unused) and the correct letter(s) |
| `source_version` | generator version of the scene sample (`procthor_v9` / `procthor_v10` / `ithor_v9` / `ithor_v10`) |
| `mover_object_id` | AI2-THOR id of the moved object |
| `internal_split` | train only: the split used in our experiments (`train0` 137,684 · `train1` 137,599 · `train2` 137,612 · `val` 1,916 · `test` 4,801). It is a held-out part of the ProcTHOR train data, separate from the iTHOR `test` split |
| `occludee_px`, `occlusion_cover`, `occlusion_relabelled` | `occlusion` only: occludee pixels in the before image, fraction covered by the mover, whether the row was relabelled from "visible" |
| `som_colors` | `colour_som` only: JSON string mapping mark id → colour (`1` mover, `2` landmark or occludee, `A`–`F` option objects) |
| `som_labels`, `som_relabelled` | `red_som` only: JSON string mapping mark id → label drawn on the badge; whether badges were redrawn with the release letters |
| `choices_match_som` | `natural` only: whether the options are the same objects in the same order as the SoM rows (if so, the answer letter matches too) |
| `cot_gt`, `cot_1`, `cot_0` | reasoning annotations (see above); `null` where not available |

**Image encoding.** Sim test images are the original PNGs. Sim train images are JPEG at quality 95, which keeps
the train parquet small.

---

## FlowBench-Real

### Source and generation

Questions come from real robot-manipulation episodes in **Bridge V2, DROID, Language-Table, RT-1 and Berkeley
UR5**. The before image is the first frame of an episode. The after image is the last frame, or for DROID a frame
after the last gripper release. The question states the moved object's **measured** displacement in centimetres,
and the after frame is the recorded outcome.

| split | questions | Language-Table | RT-1 | DROID | Bridge V2 | Berkeley UR5 |
|---|---|---|---|---|---|---|
| `train` | 8,575 (5,367 image pairs) | 3,039 | 2,144 | 1,905 | 1,449 | 38 |
| `test` | 1,083 (1,083 image pairs, one question each) | 478 | 91 | 245 | 267 | 2 |

Train and test share no image pair.

**How the answers are computed.**
1. SAM 3 segments both frames, Depth Anything 3 estimates depth for both frames in one call, and AllTracker tracks
   every pixel through the episode.
2. Objects are matched across frames by their tracks. The moved object must move at least 3 cm and landmarks
   less than 2 cm, and the tracked motion must agree with the measured displacement.
3. 3D positions are `X = z / g · K⁻¹ [u, v, 1]ᵀ`, where `g` is a divisor set for each dataset (Bridge V2 1.95, DROID
   1.15, RT-1 0.61, Berkeley UR5 0.84). For Language-Table, positions come from a table-plane homography fitted to
   the robot's recorded end-effector positions.
4. Distances are between object surfaces (centre distance minus estimated radii), and every comparison must clear
   a margin of 3 cm (6 cm for DROID).
5. Rule-based filters remove ambiguous items. Most of these filters were added after a round of human review found
   a failure case.

A VLM only names objects and checks marks; it never selects an answer. In the test set, yes/no answers are
45–55% "yes" within each category. `no_change` marks the 291 test questions whose answer the move does not change;
in all other test questions the answer differs between the before and after frames. 96 test questions were
checked by a human reviewer against the after frame and confirmed correct (`human_verified`).

The motion text gives the displacement of the object's centre in the image's left/right and up/down directions, in
whole centimetres. Components under 2 cm and the depth component are left out. Each question also includes a
scale reference measured the same way ("For scale, X is about N cm across at its widest point").

### Real row fields

| field | description |
|---|---|
| `id`, `sample_id` | question id `<source subset>__<number>` (unique across splits and identical across the two styles); image-pair id |
| `source`, `source_subset` | robot dataset; generation pool |
| `category`, `mcq_type`, `n_choices` | question category, format, number of options |
| `image_before`, `image_after` | before and after frames (640×480; DROID 912×512), original JPEG bytes |
| `scale_reference` | scale sentence (`null` on 76 train rows) |
| `motion_command` | the move, e.g. "… its center ends up about 14 cm to the left, and about 19 cm down …" |
| `question`, `static_question` | counterfactual and static question text |
| `choice_A` … `choice_F`, `answer` | options (`null` if unused) and the correct letter(s) |
| `prompt`, `static_prompt` | full text used in our evaluations (scale sentence, move, question, lettered options) |
| `named_objects` | whether this is a named-object question (no marks) |
| `no_change` | whether the move leaves the answer unchanged |
| `secondary_movers` | JSON list of other objects the robot moved in the same episode, which the move text describes |
| `provenance` | JSON with the measured quantities behind the answer (distances before and after in metres, threshold, margin, depth gap, occlusion rule, ...) |
| `som_colors` | `colour_som` only: JSON string mapping mark number → dot colour (`null` in `red_som` and on named-object rows) |
| `human_verified` | test only: `true` for the 96 questions a human checked and confirmed; `null` otherwise |

---

## Evaluation notes

- Answers are option letters `A`–`F`. For `closest_three` the answer is three letters in alphabetical order (e.g.
  `ABD`).
- Report the **counterfactual** setting (before image with `motion_command` and `question`) and the **static**
  setting (after image with `static_question`) separately. Their difference measures how much the model loses
  when it must imagine the outcome.
- In FlowBench-Sim, `contact_wall` and `near_object` are usually left out of headline averages.
- Sim `train` includes `internal_split`. For held-out evaluation, use the `test` split (iTHOR scenes for Sim,
  disjoint image pairs for Real).

## Limitations

- **Templated language.** The questions are templated by design to isolate spatial reasoning. They do not cover
  the variety of free-form instructions.
- **Sim ground truth** uses simulator geometry: bounding-box distances, AI2-THOR collision checks and wall
  clearances, and moves are stated in the camera frame.
- **Real ground truth** uses monocular depth, segmentation and tracking, so absolute centimetres are approximate.
  On Bridge V2 the size check is confounded by toy food. Relative geometry is reliable, and comparisons must
  clear a margin.
- **Coverage.** Real `depth_order` exists only for Bridge V2 and DROID. Language-Table and RT-1 have no
  named-object questions. Real train is dominated by `near_object`.
- **Dense ground truth not included.** The parquet files contain the images and question annotations. The dense
  ground truth used to build the questions (optical flow, depth, instance segmentation, moved-object masks,
  per-sample metadata) is not in the parquet files.

## Licences

<!-- TODO: state the licence of the FlowBench annotations here. -->
- FlowBench-Sim images are rendered with [AI2-THOR](https://ai2thor.allenai.org/) using ProcTHOR-10k and iTHOR
  scenes.
- FlowBench-Real frames come from Bridge V2, DROID, Language-Table, RT-1 and Berkeley UR5 and **keep their original licences**. Check the source dataset's terms before redistributing frames.

## Citation

```bibtex
@misc{flowbench,
  title  = {FlowBench},
  author = {TODO},
  year   = {2026},
  note   = {TODO: add paper reference}
}
```