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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 1 fields in line 3, saw 2

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                                       ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                         ~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                         ~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                      ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      nrows
                      ^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 3, saw 2

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GameBind counterfactual twins

Rendered two-agent scenes and their counterfactual twins, released with the anonymous submission "Counterfactual Twins Identify Perception and Binding Errors in Vision-Language Models".

Each base scene shows a target agent and a competitor agent in an 8x8 tile world. Each agent performs one action (pick up, push, hit or go to) on one object. Every action leaves a visible end state: a picked-up object is carried, a pushed object slides and leaves skid marks, a hit object cracks and leaves debris, and go to changes nothing. A twin re-renders the same scene with one controlled change, and all other pixels stay the same. Comparing a model's answers across the twins of a scene separates two causes of a wrong answer: missing the target's action and guessing, or seeing it and attributing the competitor's action to the target.

Files

file contents clips questions images
gamebind_twins_full.zip 1,000 base scenes and 300 near-condition scenes with their main twins 5,362 16,086 42,896
gamebind_twins_ext.zip further twins of the same 1,000 scenes 2,318 6,954 18,544
gamebind_twins.zip the API subset: a separate draw of 300 base scenes and 150 near-condition scenes 1,717 5,151 13,736
gamebind_twins_ext_api.zip further twins of the API subset 677 2,031 5,416
colorshape_twins.zip colour-shape displays, 600 base scenes (easy display) 4,200 4,200 3,601
colorshape_twins_hard.zip colour-shape displays, 600 base scenes (hard display) 4,200 4,200 3,601
per_scene/ every model's answers per scene and twin, from which all results of the paper are computed

Unzip all files in one folder. Each zip creates data/<set>/ with:

  • clips.json: one entry per clip, with the base scene, the twin, the events, the agents and the paths of its frames;
  • queries.json: the questions asked on each clip, with their options and the correct answer;
  • checks.json: the pixel checks of every twin;
  • summary.json: the generator settings and the check summary;
  • frames/: the images.

The frame paths in clips.json are relative to the folder where you unzip. A GameBind clip has 8 frames (f0.png to f7.png, 512x512 pixels), and the models in the paper saw the last frame, f7.png. A colour-shape clip is one 512x512 image.

Twins

twin change
S base scene
minus_c the competitor stays idle at its start tile
minus_cue the target moves as in S, but its action leaves no trace
minus_cue_minus_c both changes
minus_ccue only the competitor's trace is deleted, the competitor still moves
degraded_cue the target's trace is weakened
swap the two agents exchange their events
placebo an irrelevant change to the floor pixels
S_repro a fresh rendering of S, used to check that rendering is exact

In the colour-shape displays, the twins grey out the competitor shape (minus_c), the target shape (minus_cue) or both, add a grey shape elsewhere (placebo), or exchange the two colours (swap). A blind grey image (blind) is also included.

Questions

On every GameBind clip three questions are asked, each with four options. The joint question asks what the target agent did, and its options are the true event, two answers that mix the target's and the competitor's action and object, and the competitor's whole event. The object question and the action question ask for one part. Options are shown under rotated orders, and all twins of a base scene share the same orders. The colour-shape question asks for the colour of the target shape, with the target's colour, the competitor's colour and two absent colours as options.

Model answers

per_scene/ holds one gzip CSV per model and scene set: 19 open models on all scenes, and GPT-4.1, GPT-4o, GPT-5.5 and seven open models on the API subset. Each row is one question about one twin of one base scene, with the answer probability of every option. The subfolders colorshape/ and photo_twins/ hold the answers on the colour-shape displays and on the real-photo twins (numbers only). per_scene/README.md describes the columns.

Notes

  • All images are rendered. The dataset contains no photographs and no personal data.
  • The renderer regenerates every frame bit for bit. Code: https://anonymous.4open.science/r/counterfactual-twins-578F
  • The API subset is a separate draw from the 1,000 scenes, although the scene names repeat. A scene is identified by its set and its base_id.

Licence

CC BY 4.0.

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