| --- |
| license: other |
| license_name: per-source |
| license_link: https://huggingface.co/datasets/yrlyrl/spatial-mmcot#sources-and-licences |
| pretty_name: "Spatial MMCoT v1" |
| viewer: false |
| configs: |
| - config_name: val_keys |
| data_files: |
| - split: validation |
| path: splits/val_keys.tsv |
| sep: "\t" |
| comment: "#" |
| names: [source, key] |
| - config_name: val_members |
| data_files: |
| - split: validation |
| path: splits/val_members.tsv |
| sep: "\t" |
| comment: "#" |
| names: [source, first_image_phash] |
| --- |
| |
| # Spatial MMCoT v1 |
|
|
| Spatial reasoning data in one interleaved format: input image(s) and a question, then rounds of *plan → target image*, then a read-back and the answer. How much of that the text carries differs by source (`supervision_kind` in each source's `meta`, and each card says which): |
|
|
| - **full_interleaved** (motif, thinkmorph_jigsaw, thinkmorph_spatial_nav, zebra_jigsaw, zebra_multihop): the trace interleaves text and target images (drawn or rendered states; the source cards say which), and the read-back comes after the image it reads. |
| - **visual_aux** (ipt_mvc, ipt_pet): the text comes from a separate text-only chain of thought that never saw the generated image; use these rows as image supervision, not as evidence that drawing a state helps. |
| - **visual_only** (vdrop): no reasoning text: every thought is empty. |
| |
| ## This release: r4, the training subset |
| |
| Every thought was rewritten by Qwen3.8-27B (thinking mode, prompt v4.1) from the row's own pictures, question, options and answer; the images, questions, options and answers are those of the previous release (r3), checked row by row. This is the subset used for training: 740 rows `label_doubt`, 77 rows `over_budget`, 1 rows `R.no_rewrite` are removed (each source's `reports/subset_dropped.jsonl` names them), and two r3 sources, `cova` and `zebra_tetris`, are left out because the self-check found their long chains drift (2.1 faithfulness errors per row on both). Their repositories keep the r3 rows and are not linked below. Each source card has an r4 section with its own counts and self-check. |
|
|
| ## Known issues |
|
|
| Every source card has a **Known issues** section: rows with a measured per-row problem, listed in the source's `reports/known_issues/` so they can be filtered out (no row was removed for them), and measured caveats that cannot be listed row by row. Read it before training on a source. |
|
|
| Rows listed per issue (a row can be listed under more than one issue): |
|
|
| - `ipt_mvc`: `options_give_answer` 1,483 |
| - `ipt_pet`: `contradicting_answers_same_images` 15; `plan_turn_rule_backwards` 1 |
| - `motif`: `count_options_give_answer` 146 |
| - `thinkmorph_jigsaw`: `target_shared_with_other_row` 12; `text_states_other_arrangement` 3; `validation_target_near_copy_of_train` 1 |
| - `thinkmorph_spatial_nav`: `validation_maze_near_twin_in_train` 46; `readback_claims_path_fails` 30; `plan_misplaces_start` 4 |
| - `zebra_jigsaw`: `readback_names_no_option` 237; `text_says_options_not_shown` 25; `target_shared_with_other_row` 16; `question_states_answer` 1 |
| - `zebra_multihop`: `target_repeats_earlier_target` 665; `readback_states_no_count` 5 |
|
|
| ## Sources and licences |
|
|
| Each source is its own repository, with its own licence; the licence column gives each source's upstream licence, and each source card's Citation section names the upstream work to cite. |
|
|
| | source | train | validation | target image slots | distinct target images | licence | repo | |
| |---|---:|---:|---:|---:|---|---| |
| | ipt_mvc | 8,866 | 208 | 9,074 | 4,680 | undeclared | [yrlyrl/spatial-mmcot-ipt_mvc](https://huggingface.co/datasets/yrlyrl/spatial-mmcot-ipt_mvc) | |
| | ipt_pet | 8,409 | 211 | 8,620 | 4,968 | undeclared | [yrlyrl/spatial-mmcot-ipt_pet](https://huggingface.co/datasets/yrlyrl/spatial-mmcot-ipt_pet) | |
| | motif | 20,298 | 631 | 44,243 | 44,210 | mit | [yrlyrl/spatial-mmcot-motif](https://huggingface.co/datasets/yrlyrl/spatial-mmcot-motif) | |
| | thinkmorph_jigsaw | 5,651 | 143 | 5,794 | 5,788 | undeclared + ADE20K / SUN RGB-D terms | [yrlyrl/spatial-mmcot-thinkmorph_jigsaw](https://huggingface.co/datasets/yrlyrl/spatial-mmcot-thinkmorph_jigsaw) | |
| | thinkmorph_spatial_nav | 5,645 | 162 | 5,807 | 5,807 | undeclared | [yrlyrl/spatial-mmcot-thinkmorph_spatial_nav](https://huggingface.co/datasets/yrlyrl/spatial-mmcot-thinkmorph_spatial_nav) | |
| | vdrop | 6,543 | 264 | 6,807 | 1,152 | cc-by-4.0 | [yrlyrl/spatial-mmcot-vdrop](https://huggingface.co/datasets/yrlyrl/spatial-mmcot-vdrop) | |
| | zebra_jigsaw | 10,538 | 316 | 10,854 | 10,846 | cc-by-nc-4.0 + ImageNet terms | [yrlyrl/spatial-mmcot-zebra_jigsaw](https://huggingface.co/datasets/yrlyrl/spatial-mmcot-zebra_jigsaw) | |
| | zebra_multihop | 2,865 | 89 | 10,870 | 10,139 | cc-by-nc-4.0 | [yrlyrl/spatial-mmcot-zebra_multihop](https://huggingface.co/datasets/yrlyrl/spatial-mmcot-zebra_multihop) | |
| | **total** | **68,815** | **2,024** | **102,069** | **87,590** | | | |
| |
| Distinct target images are counted by content hash within each source. |
| |
| ## Benchmark overlap |
| |
| - `ipt_mvc`: the IPT MVC AI2-THOR evaluation set draws its options the same way as this source (four consecutive counts, the correct one never the largest), so a model trained here can gain on it from the options alone; see the source card. |
| - `ipt_pet` shares no ProcTHOR house with the IPT PET-AI2THOR evaluation set (`weikaih/imaginative-perception-token-pet-eval-ai2thor`), but every one of that set's questions uses a template found in this source's training rows, and its renders come from the same ProcTHOR renderer: that benchmark is in-domain, not out-of-distribution, for a model trained on it. |
| - `motif`: VisWorld-Eval ballgame uses the same task text as `ball_tracking_naive`, so treat it as in-domain; VisWorld-Eval multihop asks the same kind of question as `manipulation_naive` about CLEVR-style scenes (no shared images found by S11), so treat it as near-domain; `sokoban_naive` is excluded from this release (its card says why). |
| - `thinkmorph_jigsaw`: 2 puzzles whose assembled photograph is a What's Up image were removed (S11); upstream ThinkMorph Jigsaw_Assembly still contains them. |
| - `thinkmorph_spatial_nav`: mazes identical to VSP-maze evaluation items were removed (S11), but VSP-maze is in-domain (the ThinkMorph paper uses it as its in-domain benchmark for this task): it uses the same FrozenLake renderer and question text, and most of its 3x3 items have a training maze that differs only in where the start is. Only its 7x7 and 8x8 items are held out, and they are larger than any training grid (3x3 to 6x6). |
| |
| ## Files |
| |
| `parquet_info_all.json` merges the released sources' indexes for a mixed training run. Its keys are `<source>/<split>/<file>`, and the loader matches them against `os.path.join(data_dir, file)` exactly. Download each source into `<root>/<source>` (the source name, not the repository name) and either run from `<root>` with `data_dir: <source>/train`, or rewrite every key `k` to `os.path.join(root, k)` and use `data_dir: os.path.join(root, '<source>', 'train')`. Do not strip the `<source>/` prefix: the sources' file names collide. A source whose keys do not match adds no rows to the mixed group, and there is no error. |
|
|
| `num_used_data` counts chunk files, not rows: the loader repeats a source's file list up to that many entries, so a source read with the same value as the others but twice as many files has each of its rows read half as often. Give every source the same multiple of its training chunk-file count, 128 per file as below: every file is then listed 128 times, every row group is read, and each source's share is proportional to its row groups (all row groups hold up to 128 rows, so up to each file's last, partial group, to its rows). |
|
|
| | source | training chunk files | row groups | `num_used_data` | |
| |---|---:|---:|---:| |
| | ipt_mvc | 2 | 71 | 256 | |
| | ipt_pet | 1 | 66 | 128 | |
| | motif | 1 | 159 | 128 | |
| | thinkmorph_jigsaw | 1 | 45 | 128 | |
| | thinkmorph_spatial_nav | 1 | 45 | 128 | |
| | vdrop | 1 | 52 | 128 | |
| | zebra_jigsaw | 1 | 83 | 128 | |
| | zebra_multihop | 1 | 23 | 128 | |
| |
| The rows of `ipt_mvc` (4 to 5 inputs), `vdrop` (2 inputs) have more than one input image; every other source has one. A run that includes them needs a loader that reads `num_input_images` (the `UnifiedEditIterableDataset` of the IPT release, https://github.com/weikaih04/Imaginative-Perception-Token, `data/interleave_datasets/edit_dataset.py`, or ThinkMorph's with the two-line change shown in those cards). The stock ThinkMorph loader takes `image_list[0]` as the only input and trains the other inputs as generated images, without an error: on a row with two inputs and one target it trains the second input after the plan and the real target after the answer, and on a row with three or more inputs and one target it never trains the real target. |
| |
| The `zebra_*` thoughts keep upstream's 'THOUGHT n:' labels inside `<think>`; the other sources have none (the zebra cards show how to strip them). |
| |
| `reports/` holds the cross-source stage reports: `export_summary.json`, `s10_summary.json`, `s12_summary.json`, `s13_summary.json`, `s10_label_conflicts.jsonl`. The frozen S12 validation keys are in `splits/` (`val_keys.tsv`: `<source>`, `<corpus>::<key>` for keyed policies, where `<corpus>` is `meta.source_scene_corpus` and `<key>` is `meta.scene_id` (`meta.geometry_uid` for zebra_jigsaw and thinkmorph_spatial_nav), with two exceptions: ipt_mvc and ipt_pet write the ProcTHOR house as `scene-<N>`, which `meta.scene_id` spells `house_<N>` / `ProcTHOR_House_<N>`, and in cova, motif, zebra_tetris and zebra_multihop rows that share an input image carry the smallest key of their group; `val_members.tsv`: `<source>`, first-image pHash for cluster policies; `key_digest.tsv`: per-source key digests). They are split keys, not data rows; the rows are in the per-source repositories. |
| |
