README.md

metadata

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, 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: 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.

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	undeclared
messytable_relabel	leo66666/messytable	undeclared
motif	OpenRaiser/MoTiF	mit
thinkmorph_jigsaw	ThinkMorph/Jigsaw_Assembly	undeclared
thinkmorph_spatial_nav	ThinkMorph/Spatial_Navigation	undeclared
vdrop	QianYangMILA/vdrop-crossview-8k	cc-by-4.0
zebra_jigsaw	multimodal-reasoning-lab/Zebra-CoT	cc-by-nc-4.0
zebra_multihop	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.