Datasets:

yrlyrl
/

spatial-mmcot-v2

---
license: other
license_name: per-source
pretty_name: "Spatial MMCoT v2"
language:
- en
task_categories:
- visual-question-answering
configs:
- config_name: jigsaw
  default: true
  data_files:
  - split: train
    path: jigsaw/preview/train/*.parquet
  - split: validation
    path: jigsaw/preview/validation/*.parquet
- config_name: multiview_counting
  data_files:
  - split: train
    path: multiview_counting/preview/train/*.parquet
  - split: validation
    path: multiview_counting/preview/validation/*.parquet
- config_name: perspective_taking
  data_files:
  - split: train
    path: perspective_taking/preview/train/*.parquet
  - split: validation
    path: perspective_taking/preview/validation/*.parquet
- config_name: cross_view
  data_files:
  - split: train
    path: cross_view/preview/train/*.parquet
  - split: validation
    path: cross_view/preview/validation/*.parquet
- config_name: state_tracking
  data_files:
  - split: train
    path: state_tracking/preview/train/*.parquet
  - split: validation
    path: state_tracking/preview/validation/*.parquet
- config_name: navigation
  data_files:
  - split: train
    path: navigation/preview/train/*.parquet
  - split: validation
    path: navigation/preview/validation/*.parquet
---
# Spatial MMCoT v2

Interleaved image-text chain-of-thought for spatial reasoning.  Each row is a question with one or more input images,
a written plan, one or more **helper pictures** that a model is trained to draw, a reading of each picture, and the
answer.  63,871 rows (train 62,136 / validation 1,735) from 8
sources, grouped by the kind of task into the 6 subsets below -- choose one in the viewer.  The validation
split is held out by scene.

The questions, options, answers and helper pictures come from the upstream sources (see Licences); the
`messytable_relabel` questions and answers were derived again from the original MessyTable annotations.  Every thought
was written by a model: Qwen3.8-27B in thinking mode (temperature 1.0, top-p 0.95, top-k 20) was shown the row's input
images, its helper pictures in order, the question, the options and the answer, and wrote the plan before each helper
(what is still unknown, and what the picture should show to settle it) and the reading after the last one.  The text
is therefore written toward the stored answer, and it can misdescribe a picture (see the known limits below).

## Subsets

| subset | the questions | the helper picture | rows from (tasks) | train | validation |
|---|---|---|---|---:|---:|
| `jigsaw` | Jigsaw reassembly: which candidate pieces fill the grey holes of a photo, or in which order shuffled strips of a photo belong | the completed / reassembled photo | `zebra_jigsaw`, `thinkmorph_jigsaw` | 16,185 | 458 |
| `multiview_counting` | Multi-view counting: how many objects of a kind are on a cluttered table seen in 2-7 photos, or which kind is the most numerous | the real overhead photo of the same table | `messytable_relabel` | 6,334 | 107 |
| `perspective_taking` | Perspective taking: after walking to a marked X and turning 90 degrees, is an object closer / further, on the left / right | the rendered view from the X after the turn | `ipt_pet` | 8,393 | 211 |
| `cross_view` | Cross-view reasoning: objects seen from two spots of one room: which one shows in both, which is nearest to a named object, how many | a wide panorama of the room (it does not wrap around) | `vdrop` | 3,990 | 166 |
| `state_tracking` | Multi-step state tracking: where a bouncing ball exits, what a scene of solids looks like after a sequence of edits, how many remain | the trajectory or scene after each step, one picture per step | `motif` (ball_tracking_naive, manipulation_naive), `zebra_multihop` | 14,632 | 445 |
| `navigation` | Navigation and route planning: the move sequence across a frozen lake avoiding holes, or the number of turns on the route through a maze | the grid with the route drawn | `thinkmorph_spatial_nav`, `motif` (maze_naive) | 12,602 | 348 |
## What a row contains (the viewer)
Columns from left to right: `source` and `task` (the upstream source and task), `question` and `options` as the
model receives them (the system prompt is in the training files), `answer`, `input_image_0..` (the inputs), then the
chain in the order the model writes it -- `thought_0` (the plan), `target_image_0` (the helper picture), `thought_1`,
`target_image_1`, ... -- and the last `thought_` column (the reading of the last helper and the conclusion).  Image
and thought slots past the row's own are null.  Then `row_uid`, `category`, `num_input_images` and `num_steps` (K,
the number of helpers), `split`, `license` (the row's licence), `source_scene_corpus`, `est_tokens` and
`filter_flags` (the flags below).

## How v2 differs from v1

v2 is v1 ([yrlyrl/spatial-mmcot](https://huggingface.co/datasets/yrlyrl/spatial-mmcot), one repository per source, same
images and text) after an audit of the training mixture (a stratified review of 270 rows and mechanical checks over
every row):

- `ipt_mvc` is removed (counts that miss visible paintings, hidden type conventions, options that give the answer, a
  top-down map that cannot show what is counted).
- the cross-view questions that ask a direction -- from the viewpoint of one photo (`perspective_taking`) or between two objects (`relative_direction`) -- are removed (`vdrop_helper_misleads_direction` below): their helper panorama is taken from a third spot, and answering with it made a model worse than answering without it (Qwen3.8-27B with thinking, the same 29 sampled questions with and without the panorama: -21 and -13 points).
- rows removed, by reason:

| reason | rows |
|---|---:|
| vdrop_helper_misleads_direction | 2,616 |
| motif_answer_in_operations | 741 |
| multihop_unstated_removal | 562 |
| multihop_text_determined | 280 |
| validation_maze_near_twin_in_train | 46 |
| vdrop_broken_panorama | 35 |
| readback_claims_path_fails | 30 |
| contradicting_answers_same_images | 15 |
| plan_misplaces_start | 4 |
| text_states_other_arrangement | 3 |
| plan_turn_rule_backwards | 1 |
| validation_target_near_copy_of_train | 1 |
| question_states_answer | 1 |

- repaired in place: the frozen-lake instruction's `\boxed{{}}` (now `\boxed{}`); ProcTHOR one-word type names in the
  perspective-taking questions and texts (`diningtable` -> `dining table`); "360-degree panorama" wording in the
  cross-view texts.
- easy or redundant rows are kept and **flagged** in `filter_flags`, so a training run can choose its subset:

| flag | meaning | rows |
|---|---|---|
| `A.vdrop_pair_n=<n>` | rows that share this row's two input views (and so its panorama) | cross_view 4,156 |
| `A.nav_moves=<n>` | number of moves in a frozen-lake answer | navigation 5,727 |
| `A.manip_count` | a manipulation question that asks for a count (object positions do not matter) | state_tracking 1,782 |
| `A.vdrop_wrap_claim` | the text says the panorama wraps around (it does not) | cross_view 464 |
| `A.multihop_untouched` | no operation can change the counted class (the answer is the initial count) | state_tracking 574 |
| `A.repeat_target` | a helper repeats an earlier helper of the row (a no-op step) | state_tracking 564 |
| `A.text_issue=<name>` | a text-only issue found by a rule (the label and pictures are fine) | jigsaw 261, state_tracking 5 |
| `A.options_give_answer` | the four consecutive numeric options alone give the answer | state_tracking 146 |
| `A.shared_target` | the same photo is the target of another puzzle row | jigsaw 28 |

Known limits (measured in the audit, not fixed): about half of the rows have at least one statement in the text that
does not match the pictures (wrong object or shape names, miscounts of incidental objects), while answers and helpers
are almost always right; the jigsaw decoy pieces are crops of the visible photo; several upstream panoramas are taken
from a third spot rather than from either input camera.

## Training files

Each subset directory also holds the training format: `<subset>/train/*.parquet` and `<subset>/validation/*.parquet`
(`image_list` list<binary>: the inputs then the helpers; `num_input_images`; `instruction_list` with the system prompt,
question and options; `output_text_list` with the K+1 segments `<think>..</think><image_start>` ...
`<image_end><think>..</think><answer>..</answer>`; `row_uid`), `<subset>/meta/` (the per-row sidecar, same order) and
`<subset>/parquet_info.json` (keys `<subset>/<split>/<file>`).  The three formats are aligned file by file and row by
row.

```python
from datasets import load_dataset
rows = load_dataset("yrlyrl/spatial-mmcot-v2", "perspective_taking", split="train")      # the viewer's rows
from huggingface_hub import snapshot_download                                            # the training format
root = snapshot_download("yrlyrl/spatial-mmcot-v2", repo_type="dataset",
                         allow_patterns=["navigation/train/*", "navigation/validation/*", "navigation/parquet_info.json"])
# BAGEL / ThinkMorph trainer: run from `root` with data_dir: navigation/train, parquet_info_path: navigation/parquet_info.json
```

## Licences

Each row keeps the licence of its upstream source (the `license` column):

| source | upstream | licence |
|---|---|---|
| `ipt_pet` | [weikaih/imaginative-perception-token-pet-ipt](https://huggingface.co/datasets/weikaih/imaginative-perception-token-pet-ipt) | undeclared |
| `messytable_relabel` | [leo66666/messytable](https://huggingface.co/datasets/leo66666/messytable) | undeclared |
| `motif` | [OpenRaiser/MoTiF](https://huggingface.co/datasets/OpenRaiser/MoTiF) | mit |
| `thinkmorph_jigsaw` | [ThinkMorph/Jigsaw_Assembly](https://huggingface.co/datasets/ThinkMorph/Jigsaw_Assembly) | undeclared |
| `thinkmorph_spatial_nav` | [ThinkMorph/Spatial_Navigation](https://huggingface.co/datasets/ThinkMorph/Spatial_Navigation) | undeclared |
| `vdrop` | [QianYangMILA/vdrop-crossview-8k](https://huggingface.co/datasets/QianYangMILA/vdrop-crossview-8k) | cc-by-4.0 |
| `zebra_jigsaw` | [multimodal-reasoning-lab/Zebra-CoT](https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT) | cc-by-nc-4.0 |
| `zebra_multihop` | [multimodal-reasoning-lab/Zebra-CoT](https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT) | cc-by-nc-4.0 |

Sources whose upstream declares no licence (`ipt_pet`, `messytable_relabel`, `thinkmorph_jigsaw`, `thinkmorph_spatial_nav`) are shared for research use only; the upstream terms apply, and the copy is removed on the authors' request.

	---
	license: other
	license_name: per-source
	pretty_name: "Spatial MMCoT v2"
	language:
	- en
	task_categories:
	- visual-question-answering
	configs:
	- config_name: jigsaw
	default: true
	data_files:
	- split: train
	path: jigsaw/preview/train/*.parquet
	- split: validation
	path: jigsaw/preview/validation/*.parquet
	- config_name: multiview_counting
	data_files:
	- split: train
	path: multiview_counting/preview/train/*.parquet
	- split: validation
	path: multiview_counting/preview/validation/*.parquet
	- config_name: perspective_taking
	data_files:
	- split: train
	path: perspective_taking/preview/train/*.parquet
	- split: validation
	path: perspective_taking/preview/validation/*.parquet
	- config_name: cross_view
	data_files:
	- split: train
	path: cross_view/preview/train/*.parquet
	- split: validation
	path: cross_view/preview/validation/*.parquet
	- config_name: state_tracking
	data_files:
	- split: train
	path: state_tracking/preview/train/*.parquet
	- split: validation
	path: state_tracking/preview/validation/*.parquet
	- config_name: navigation
	data_files:
	- split: train
	path: navigation/preview/train/*.parquet
	- split: validation
	path: navigation/preview/validation/*.parquet
	---

	# Spatial MMCoT v2

	Interleaved image-text chain-of-thought for spatial reasoning. Each row is a question with one or more input images,
	a written plan, one or more helper pictures that a model is trained to draw, a reading of each picture, and the
	answer. 63,871 rows (train 62,136 / validation 1,735) from 8
	sources, grouped by the kind of task into the 6 subsets below -- choose one in the viewer. The validation
	split is held out by scene.

	The questions, options, answers and helper pictures come from the upstream sources (see Licences); the
	`messytable_relabel` questions and answers were derived again from the original MessyTable annotations. Every thought
	was written by a model: Qwen3.8-27B in thinking mode (temperature 1.0, top-p 0.95, top-k 20) was shown the row's input
	images, its helper pictures in order, the question, the options and the answer, and wrote the plan before each helper
	(what is still unknown, and what the picture should show to settle it) and the reading after the last one. The text
	is therefore written toward the stored answer, and it can misdescribe a picture (see the known limits below).

	## Subsets

	\| subset \| the questions \| the helper picture \| rows from (tasks) \| train \| validation \|
	\|---\|---\|---\|---\|---:\|---:\|
	\| `jigsaw` \| Jigsaw reassembly: which candidate pieces fill the grey holes of a photo, or in which order shuffled strips of a photo belong \| the completed / reassembled photo \| `zebra_jigsaw`, `thinkmorph_jigsaw` \| 16,185 \| 458 \|
	\| `multiview_counting` \| Multi-view counting: how many objects of a kind are on a cluttered table seen in 2-7 photos, or which kind is the most numerous \| the real overhead photo of the same table \| `messytable_relabel` \| 6,334 \| 107 \|
	\| `perspective_taking` \| Perspective taking: after walking to a marked X and turning 90 degrees, is an object closer / further, on the left / right \| the rendered view from the X after the turn \| `ipt_pet` \| 8,393 \| 211 \|
	\| `cross_view` \| Cross-view reasoning: objects seen from two spots of one room: which one shows in both, which is nearest to a named object, how many \| a wide panorama of the room (it does not wrap around) \| `vdrop` \| 3,990 \| 166 \|
	\| `state_tracking` \| Multi-step state tracking: where a bouncing ball exits, what a scene of solids looks like after a sequence of edits, how many remain \| the trajectory or scene after each step, one picture per step \| `motif` (ball_tracking_naive, manipulation_naive), `zebra_multihop` \| 14,632 \| 445 \|
	\| `navigation` \| Navigation and route planning: the move sequence across a frozen lake avoiding holes, or the number of turns on the route through a maze \| the grid with the route drawn \| `thinkmorph_spatial_nav`, `motif` (maze_naive) \| 12,602 \| 348 \|

	## What a row contains (the viewer)

	Columns from left to right: `source` and `task` (the upstream source and task), `question` and `options` as the
	model receives them (the system prompt is in the training files), `answer`, `input_image_0..` (the inputs), then the
	chain in the order the model writes it -- `thought_0` (the plan), `target_image_0` (the helper picture), `thought_1`,
	`target_image_1`, ... -- and the last `thought_` column (the reading of the last helper and the conclusion). Image
	and thought slots past the row's own are null. Then `row_uid`, `category`, `num_input_images` and `num_steps` (K,
	the number of helpers), `split`, `license` (the row's licence), `source_scene_corpus`, `est_tokens` and
	`filter_flags` (the flags below).

	## How v2 differs from v1

	v2 is v1 ([yrlyrl/spatial-mmcot](https://huggingface.co/datasets/yrlyrl/spatial-mmcot), one repository per source, same
	images and text) after an audit of the training mixture (a stratified review of 270 rows and mechanical checks over
	every row):

	- `ipt_mvc` is removed (counts that miss visible paintings, hidden type conventions, options that give the answer, a
	top-down map that cannot show what is counted).
	- the cross-view questions that ask a direction -- from the viewpoint of one photo (`perspective_taking`) or between two objects (`relative_direction`) -- are removed (`vdrop_helper_misleads_direction` below): their helper panorama is taken from a third spot, and answering with it made a model worse than answering without it (Qwen3.8-27B with thinking, the same 29 sampled questions with and without the panorama: -21 and -13 points).
	- rows removed, by reason:

	\| reason \| rows \|
	\|---\|---:\|
	\| vdrop_helper_misleads_direction \| 2,616 \|
	\| motif_answer_in_operations \| 741 \|
	\| multihop_unstated_removal \| 562 \|
	\| multihop_text_determined \| 280 \|
	\| validation_maze_near_twin_in_train \| 46 \|
	\| vdrop_broken_panorama \| 35 \|
	\| readback_claims_path_fails \| 30 \|
	\| contradicting_answers_same_images \| 15 \|
	\| plan_misplaces_start \| 4 \|
	\| text_states_other_arrangement \| 3 \|
	\| plan_turn_rule_backwards \| 1 \|
	\| validation_target_near_copy_of_train \| 1 \|
	\| question_states_answer \| 1 \|

	- repaired in place: the frozen-lake instruction's `\boxed{{}}` (now `\boxed{}`); ProcTHOR one-word type names in the
	perspective-taking questions and texts (`diningtable` -> `dining table`); "360-degree panorama" wording in the
	cross-view texts.
	- easy or redundant rows are kept and flagged in `filter_flags`, so a training run can choose its subset:

	\| flag \| meaning \| rows \|
	\|---\|---\|---\|
	\| `A.vdrop_pair_n=<n>` \| rows that share this row's two input views (and so its panorama) \| cross_view 4,156 \|
	\| `A.nav_moves=<n>` \| number of moves in a frozen-lake answer \| navigation 5,727 \|
	\| `A.manip_count` \| a manipulation question that asks for a count (object positions do not matter) \| state_tracking 1,782 \|
	\| `A.vdrop_wrap_claim` \| the text says the panorama wraps around (it does not) \| cross_view 464 \|
	\| `A.multihop_untouched` \| no operation can change the counted class (the answer is the initial count) \| state_tracking 574 \|
	\| `A.repeat_target` \| a helper repeats an earlier helper of the row (a no-op step) \| state_tracking 564 \|
	\| `A.text_issue=<name>` \| a text-only issue found by a rule (the label and pictures are fine) \| jigsaw 261, state_tracking 5 \|
	\| `A.options_give_answer` \| the four consecutive numeric options alone give the answer \| state_tracking 146 \|
	\| `A.shared_target` \| the same photo is the target of another puzzle row \| jigsaw 28 \|

	Known limits (measured in the audit, not fixed): about half of the rows have at least one statement in the text that
	does not match the pictures (wrong object or shape names, miscounts of incidental objects), while answers and helpers
	are almost always right; the jigsaw decoy pieces are crops of the visible photo; several upstream panoramas are taken
	from a third spot rather than from either input camera.

	## Training files

	Each subset directory also holds the training format: `<subset>/train/.parquet` and `<subset>/validation/.parquet`
	(`image_list` list<binary>: the inputs then the helpers; `num_input_images`; `instruction_list` with the system prompt,
	question and options; `output_text_list` with the K+1 segments `<think>..</think><image_start>` ...
	`<image_end><think>..</think><answer>..</answer>`; `row_uid`), `<subset>/meta/` (the per-row sidecar, same order) and
	`<subset>/parquet_info.json` (keys `<subset>/<split>/<file>`). The three formats are aligned file by file and row by
	row.

	```python
	from datasets import load_dataset
	rows = load_dataset("yrlyrl/spatial-mmcot-v2", "perspective_taking", split="train") # the viewer's rows

	from huggingface_hub import snapshot_download # the training format
	root = snapshot_download("yrlyrl/spatial-mmcot-v2", repo_type="dataset",
	allow_patterns=["navigation/train/", "navigation/validation/", "navigation/parquet_info.json"])
	# BAGEL / ThinkMorph trainer: run from `root` with data_dir: navigation/train, parquet_info_path: navigation/parquet_info.json
	```

	## Licences

	Each row keeps the licence of its upstream source (the `license` column):

	\| source \| upstream \| licence \|
	\|---\|---\|---\|
	\| `ipt_pet` \| [weikaih/imaginative-perception-token-pet-ipt](https://huggingface.co/datasets/weikaih/imaginative-perception-token-pet-ipt) \| undeclared \|
	\| `messytable_relabel` \| [leo66666/messytable](https://huggingface.co/datasets/leo66666/messytable) \| undeclared \|
	\| `motif` \| [OpenRaiser/MoTiF](https://huggingface.co/datasets/OpenRaiser/MoTiF) \| mit \|
	\| `thinkmorph_jigsaw` \| [ThinkMorph/Jigsaw_Assembly](https://huggingface.co/datasets/ThinkMorph/Jigsaw_Assembly) \| undeclared \|
	\| `thinkmorph_spatial_nav` \| [ThinkMorph/Spatial_Navigation](https://huggingface.co/datasets/ThinkMorph/Spatial_Navigation) \| undeclared \|
	\| `vdrop` \| [QianYangMILA/vdrop-crossview-8k](https://huggingface.co/datasets/QianYangMILA/vdrop-crossview-8k) \| cc-by-4.0 \|
	\| `zebra_jigsaw` \| [multimodal-reasoning-lab/Zebra-CoT](https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT) \| cc-by-nc-4.0 \|
	\| `zebra_multihop` \| [multimodal-reasoning-lab/Zebra-CoT](https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT) \| cc-by-nc-4.0 \|

	Sources whose upstream declares no licence (`ipt_pet`, `messytable_relabel`, `thinkmorph_jigsaw`, `thinkmorph_spatial_nav`) are shared for research use only; the upstream terms apply, and the copy is removed on the authors' request.