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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- visual-question-answering
+language:
+- en
+pretty_name: BedBench
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: T1_uncolor
+  data_files:
+  - split: train
+    path: data/T1_uncolor/train-*.parquet
+- config_name: T1_uncolor_axes
+  data_files:
+  - split: train
+    path: data/T1_uncolor_axes/train-*.parquet
+- config_name: T1_color
+  data_files:
+  - split: train
+    path: data/T1_color/train-*.parquet
+- config_name: T2_uncolor
+  data_files:
+  - split: train
+    path: data/T2_uncolor/train-*.parquet
+- config_name: T2_uncolor_axes
+  data_files:
+  - split: train
+    path: data/T2_uncolor_axes/train-*.parquet
+- config_name: T3_uncolor_axes
+  data_files:
+  - split: train
+    path: data/T3_uncolor_axes/train-*.parquet
+- config_name: T3_color_axes
+  data_files:
+  - split: train
+    path: data/T3_color_axes/train-*.parquet
+- config_name: T4_color
+  data_files:
+  - split: train
+    path: data/T4_color/train-*.parquet
+- config_name: T4_color_axes
+  data_files:
+  - split: train
+    path: data/T4_color_axes/train-*.parquet
+- config_name: T5_color
+  data_files:
+  - split: train
+    path: data/T5_color/train-*.parquet
+- config_name: T5_color_axes
+  data_files:
+  - split: train
+    path: data/T5_color_axes/train-*.parquet
+- config_name: T5_finetune
+  data_files:
+  - split: train
+    path: data/T5_finetune/train-*.parquet
+  - split: validation
+    path: data/T5_finetune/validation-*.parquet
+- config_name: T6_uncolor
+  data_files:
+  - split: train
+    path: data/T6_uncolor/train-*.parquet
+- config_name: T6_uncolor_axes
+  data_files:
+  - split: train
+    path: data/T6_uncolor_axes/train-*.parquet
+- config_name: T7_color
+  data_files:
+  - split: train
+    path: data/T7_color/train-*.parquet
+- config_name: T7_color_axes
+  data_files:
+  - split: train
+    path: data/T7_color_axes/train-*.parquet
+- config_name: T8_uncolor
+  data_files:
+  - split: train
+    path: data/T8_uncolor/train-*.parquet
+- config_name: T8_uncolor_axes
+  data_files:
+  - split: train
+    path: data/T8_uncolor_axes/train-*.parquet
+- config_name: T9_uncolor
+  data_files:
+  - split: train
+    path: data/T9_uncolor/train-*.parquet
+- config_name: T9_uncolor_axes
+  data_files:
+  - split: train
+    path: data/T9_uncolor_axes/train-*.parquet
+- config_name: T10_color
+  data_files:
+  - split: train
+    path: data/T10_color/train-*.parquet
+- config_name: T10_color_axes
+  data_files:
+  - split: train
+    path: data/T10_color_axes/train-*.parquet
+- config_name: T11_invalid_uncolor
+  data_files:
+  - split: train
+    path: data/T11_invalid_uncolor/train-*.parquet
+- config_name: T11_invalid_uncolor_axes
+  data_files:
+  - split: train
+    path: data/T11_invalid_uncolor_axes/train-*.parquet
+- config_name: T11_invalid_color
+  data_files:
+  - split: train
+    path: data/T11_invalid_color/train-*.parquet
+- config_name: T11_valid_uncolor
+  data_files:
+  - split: train
+    path: data/T11_valid_uncolor/train-*.parquet
+- config_name: T11_valid_uncolor_axes
+  data_files:
+  - split: train
+    path: data/T11_valid_uncolor_axes/train-*.parquet
+- config_name: T11_valid_color
+  data_files:
+  - split: train
+    path: data/T11_valid_color/train-*.parquet
+- config_name: T12_uncolor
+  data_files:
+  - split: train
+    path: data/T12_uncolor/train-*.parquet
+- config_name: T12_uncolor_axes
+  data_files:
+  - split: train
+    path: data/T12_uncolor_axes/train-*.parquet
+- config_name: T12_color
+  data_files:
+  - split: train
+    path: data/T12_color/train-*.parquet
+- config_name: T13_invalid_uncolor
+  data_files:
+  - split: train
+    path: data/T13_invalid_uncolor/train-*.parquet
+- config_name: T13_invalid_color
+  data_files:
+  - split: train
+    path: data/T13_invalid_color/train-*.parquet
+- config_name: T13_valid_uncolor
+  data_files:
+  - split: train
+    path: data/T13_valid_uncolor/train-*.parquet
+- config_name: T13_valid_color
+  data_files:
+  - split: train
+    path: data/T13_valid_color/train-*.parquet
+---
+# BedBench
+
+Evaluation data, model outputs, and probing features for the paper
+*Where Do Vision-Language Models Fail at 3D Reasoning?* Every task is posed on rendered views of 100 procedurally generated voxel objects (T1-T12) or 100
+multi-object scenes (T13). Code (generator, evaluation harness, probing, fine-tuning) is released separately.
+
+## Configurations
+
+Each configuration is one task under one visual condition (the default setting is listed first).
+
+| Task | Configurations                                                                                                               |
+| ---- | ---------------------------------------------------------------------------------------------------------------------------- |
+| T1   | `uncolor`, `uncolor_axes`, `color`                                                                                     |
+| T2   | `uncolor`, `uncolor_axes`                                                                                                |
+| T3   | `uncolor_axes`, `color_axes`                                                                                             |
+| T4   | `color`, `color_axes`                                                                                                    |
+| T5   | `color`, `color_axes`                                                                                                    |
+| T6   | `uncolor`, `uncolor_axes`                                                                                                |
+| T7   | `color`, `color_axes`                                                                                                    |
+| T8   | `uncolor`, `uncolor_axes`                                                                                                |
+| T9   | `uncolor`, `uncolor_axes`                                                                                                |
+| T10  | `color`, `color_axes`                                                                                                    |
+| T11  | `invalid_uncolor`, `invalid_uncolor_axes`, `invalid_color`, `valid_uncolor`, `valid_uncolor_axes`, `valid_color` |
+| T12  | `uncolor`, `uncolor_axes`, `color`                                                                                     |
+| T13  | `invalid_uncolor`, `invalid_color`, `valid_uncolor`, `valid_color`                                                   |
+
+`T5_finetune` holds the 800 training and 100 validation objects of the T5 fine-tuning experiment (generator seed
+100; no object overlaps the benchmark).
+
+```python
+from datasets import load_dataset
+ds = load_dataset("wnkh/BedBench", "T5_color", split="train")
+```
+
+The evaluation harness reads this repository directly: `python -m bed_eval run --dataset wnkh/BedBench ...`.
+
+## Extras
+
+| File                               | Contents                                                                                                                                                                                                                                                             |
+| ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `extras/samples.tar.gz`          | the 100 objects (manifest with all targets, every rendered view, projections, distractors) and the 100 T13 scenes, as written by the generator                                                                                                                       |
+| `extras/outputs.tar.gz`          | every model response used in the paper (12 open-weight models x 2 modes x 5 runs, 4 frontier models, image-removal, answer-format, reasoning-budget, factorized-T5, and fine-tuned runs) with ground truth;`bed_eval score outputs` reproduces the paper's numbers |
+| `extras/probing_features.tar` | mean-pooled features of the 14 frozen encoders used in the probing analysis                                                                                                                                                                                          |
+
+## License
+
+CC BY 4.0.

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