Structural Intelligence
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sal-torch by Cognitive Engineering (Switzerland). pip install sal-torch • 4 items • Updated
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
config: struct<model: string, task: string, n_train: int64, n_eval: int64, epochs: int64, lr: double, train_ (... 258 chars omitted)
child 0, model: string
child 1, task: string
child 2, n_train: int64
child 3, n_eval: int64
child 4, epochs: int64
child 5, lr: double
child 6, train_bs: int64
child 7, grad_accum: int64
child 8, eval_bs: int64
child 9, max_len: int64
child 10, prune_fraction: double
child 11, battery_seed_offset: int64
child 12, smoke: bool
child 13, finetune: string
child 14, seeds: list<item: int64>
child 0, item: int64
child 15, eval_split: string
child 16, head_selection: string
child 17, int8_backend: string
child 18, int4_backend: string
seeds_completed: list<item: int64>
child 0, item: int64
failures: struct<>
runs: struct<42: struct<seed: int64, model: string, task: string, finetune: string, epochs: int64, lr: dou (... 8136 chars omitted)
child 0, 42: struct<seed: int64, model: string, task: string, finetune: string, epochs: int64, lr: double, batch_ (... 1539 chars omitted)
child 0, seed: int64
child 1, model: string
child 2, task: string
child 3, finetune: string
child 4, epochs: int64
child 5, lr: double
child 6, batch_size: int64
child 7, n_train: int64
child 8, n_eval: int64
child 9, prune_fraction: double
child 10, battery_seed: int64
child 11, baseline: struct<dense: struct<accuracy: double, size_mb: double, backend: null, devic
...
, sal_std: double, ctrl_mean: double, ctrl_std: double, delta_mean: double, s (... 152 chars omitted)
child 0, sal_mean: double
child 1, sal_std: double
child 2, ctrl_mean: double
child 3, ctrl_std: double
child 4, delta_mean: double
child 5, sal_wins: int64
child 6, n_seeds: int64
child 7, sal_per_seed: struct<42: double, 123: double, 456: double>
child 0, 42: double
child 1, 123: double
child 2, 456: double
child 8, ctrl_per_seed: struct<42: double, 123: double, 456: double>
child 0, 42: double
child 1, 123: double
child 2, 456: double
child 2, cka: struct<sal_mean: double, sal_std: double, ctrl_mean: double, ctrl_std: double, delta_mean: double, s (... 152 chars omitted)
child 0, sal_mean: double
child 1, sal_std: double
child 2, ctrl_mean: double
child 3, ctrl_std: double
child 4, delta_mean: double
child 5, sal_wins: int64
child 6, n_seeds: int64
child 7, sal_per_seed: struct<42: double, 123: double, 456: double>
child 0, 42: double
child 1, 123: double
child 2, 456: double
child 8, ctrl_per_seed: struct<42: double, 123: double, 456: double>
child 0, 42: double
child 1, 123: double
child 2, 456: double
sal_wins_total: int64
to
{'model': Value('string'), 'dataset': Value('string'), 'epochs': Value('int64'), 'mask_ratio': Value('float64'), 'seeds': List(Value('int64')), 'std': Value('string'), 'win_rule': Value('string'), 'settings': {'random-33%': {'probe': {'sal_mean': Value('float64'), 'sal_std': Value('float64'), 'ctrl_mean': Value('float64'), 'ctrl_std': Value('float64'), 'delta_mean': Value('float64'), 'sal_wins': Value('int64'), 'n_seeds': Value('int64'), 'sal_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}, 'ctrl_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}}, 'knn': {'sal_mean': Value('float64'), 'sal_std': Value('float64'), 'ctrl_mean': Value('float64'), 'ctrl_std': Value('float64'), 'delta_mean': Value('float64'), 'sal_wins': Value('int64'), 'n_seeds': Value('int64'), 'sal_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}, 'ctrl_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}}, 'cka': {'sal_mean': Value('float64'), 'sal_std': Value('float64'), 'ctrl_mean': Value('float64'), 'ctrl_std': Value('float64'), 'delta_mean': Value('float64'), 'sal_wins': Value('int64'), 'n_seeds': Value('int64'), 'sal_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}, 'ctrl_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}}}, 'random-50%': {'probe': {'sal_mean': Value('float64'), 'sal_std': Val
...
float64')}}, 'knn': {'sal_mean': Value('float64'), 'sal_std': Value('float64'), 'ctrl_mean': Value('float64'), 'ctrl_std': Value('float64'), 'delta_mean': Value('float64'), 'sal_wins': Value('int64'), 'n_seeds': Value('int64'), 'sal_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}, 'ctrl_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}}, 'cka': {'sal_mean': Value('float64'), 'sal_std': Value('float64'), 'ctrl_mean': Value('float64'), 'ctrl_std': Value('float64'), 'delta_mean': Value('float64'), 'sal_wins': Value('int64'), 'n_seeds': Value('int64'), 'sal_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}, 'ctrl_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}}}}, 'unpruned': {'sal-trained': {'probe': {'mean': Value('float64'), 'std': Value('float64')}, 'knn': {'mean': Value('float64'), 'std': Value('float64')}, 'cka': {'mean': Value('float64'), 'std': Value('float64')}}, 'control-trained': {'probe': {'mean': Value('float64'), 'std': Value('float64')}, 'knn': {'mean': Value('float64'), 'std': Value('float64')}, 'cka': {'mean': Value('float64'), 'std': Value('float64')}}}, 'wins_per_seed': {'42': Value('int64'), '123': Value('int64'), '456': Value('int64')}, 'sal_wins_total': Value('int64'), 'comparisons_total': Value('int64'), 'source_files': List(Value('string')), 'environment_note': Value('string'), 'variance_note': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
config: struct<model: string, task: string, n_train: int64, n_eval: int64, epochs: int64, lr: double, train_ (... 258 chars omitted)
child 0, model: string
child 1, task: string
child 2, n_train: int64
child 3, n_eval: int64
child 4, epochs: int64
child 5, lr: double
child 6, train_bs: int64
child 7, grad_accum: int64
child 8, eval_bs: int64
child 9, max_len: int64
child 10, prune_fraction: double
child 11, battery_seed_offset: int64
child 12, smoke: bool
child 13, finetune: string
child 14, seeds: list<item: int64>
child 0, item: int64
child 15, eval_split: string
child 16, head_selection: string
child 17, int8_backend: string
child 18, int4_backend: string
seeds_completed: list<item: int64>
child 0, item: int64
failures: struct<>
runs: struct<42: struct<seed: int64, model: string, task: string, finetune: string, epochs: int64, lr: dou (... 8136 chars omitted)
child 0, 42: struct<seed: int64, model: string, task: string, finetune: string, epochs: int64, lr: double, batch_ (... 1539 chars omitted)
child 0, seed: int64
child 1, model: string
child 2, task: string
child 3, finetune: string
child 4, epochs: int64
child 5, lr: double
child 6, batch_size: int64
child 7, n_train: int64
child 8, n_eval: int64
child 9, prune_fraction: double
child 10, battery_seed: int64
child 11, baseline: struct<dense: struct<accuracy: double, size_mb: double, backend: null, devic
...
, sal_std: double, ctrl_mean: double, ctrl_std: double, delta_mean: double, s (... 152 chars omitted)
child 0, sal_mean: double
child 1, sal_std: double
child 2, ctrl_mean: double
child 3, ctrl_std: double
child 4, delta_mean: double
child 5, sal_wins: int64
child 6, n_seeds: int64
child 7, sal_per_seed: struct<42: double, 123: double, 456: double>
child 0, 42: double
child 1, 123: double
child 2, 456: double
child 8, ctrl_per_seed: struct<42: double, 123: double, 456: double>
child 0, 42: double
child 1, 123: double
child 2, 456: double
child 2, cka: struct<sal_mean: double, sal_std: double, ctrl_mean: double, ctrl_std: double, delta_mean: double, s (... 152 chars omitted)
child 0, sal_mean: double
child 1, sal_std: double
child 2, ctrl_mean: double
child 3, ctrl_std: double
child 4, delta_mean: double
child 5, sal_wins: int64
child 6, n_seeds: int64
child 7, sal_per_seed: struct<42: double, 123: double, 456: double>
child 0, 42: double
child 1, 123: double
child 2, 456: double
child 8, ctrl_per_seed: struct<42: double, 123: double, 456: double>
child 0, 42: double
child 1, 123: double
child 2, 456: double
sal_wins_total: int64
to
{'model': Value('string'), 'dataset': Value('string'), 'epochs': Value('int64'), 'mask_ratio': Value('float64'), 'seeds': List(Value('int64')), 'std': Value('string'), 'win_rule': Value('string'), 'settings': {'random-33%': {'probe': {'sal_mean': Value('float64'), 'sal_std': Value('float64'), 'ctrl_mean': Value('float64'), 'ctrl_std': Value('float64'), 'delta_mean': Value('float64'), 'sal_wins': Value('int64'), 'n_seeds': Value('int64'), 'sal_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}, 'ctrl_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}}, 'knn': {'sal_mean': Value('float64'), 'sal_std': Value('float64'), 'ctrl_mean': Value('float64'), 'ctrl_std': Value('float64'), 'delta_mean': Value('float64'), 'sal_wins': Value('int64'), 'n_seeds': Value('int64'), 'sal_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}, 'ctrl_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}}, 'cka': {'sal_mean': Value('float64'), 'sal_std': Value('float64'), 'ctrl_mean': Value('float64'), 'ctrl_std': Value('float64'), 'delta_mean': Value('float64'), 'sal_wins': Value('int64'), 'n_seeds': Value('int64'), 'sal_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}, 'ctrl_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}}}, 'random-50%': {'probe': {'sal_mean': Value('float64'), 'sal_std': Val
...
float64')}}, 'knn': {'sal_mean': Value('float64'), 'sal_std': Value('float64'), 'ctrl_mean': Value('float64'), 'ctrl_std': Value('float64'), 'delta_mean': Value('float64'), 'sal_wins': Value('int64'), 'n_seeds': Value('int64'), 'sal_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}, 'ctrl_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}}, 'cka': {'sal_mean': Value('float64'), 'sal_std': Value('float64'), 'ctrl_mean': Value('float64'), 'ctrl_std': Value('float64'), 'delta_mean': Value('float64'), 'sal_wins': Value('int64'), 'n_seeds': Value('int64'), 'sal_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}, 'ctrl_per_seed': {'42': Value('float64'), '123': Value('float64'), '456': Value('float64')}}}}, 'unpruned': {'sal-trained': {'probe': {'mean': Value('float64'), 'std': Value('float64')}, 'knn': {'mean': Value('float64'), 'std': Value('float64')}, 'cka': {'mean': Value('float64'), 'std': Value('float64')}}, 'control-trained': {'probe': {'mean': Value('float64'), 'std': Value('float64')}, 'knn': {'mean': Value('float64'), 'std': Value('float64')}, 'cka': {'mean': Value('float64'), 'std': Value('float64')}}}, 'wins_per_seed': {'42': Value('int64'), '123': Value('int64'), '456': Value('int64')}, 'sal_wins_total': Value('int64'), 'comparisons_total': Value('int64'), 'source_files': List(Value('string')), 'environment_note': Value('string'), 'variance_note': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
sal-torch Benchmarks
Raw results from sal-torch experiments: SAL-trained vs standard-trained models under compression.
Files
multiseed_results.json— GPT-2 Medium / SST-2, 5 seeds, full 872-example validation splitrobustness_scale_results.json— robustness suite across model scalesjepa_sal_multiseed_summary.json— I-JEPA ViT-H/14 / ImageNet-100, 3 seedssal_qat_prototype_seed{42,123,456}.json— SAL+QAT on ViT-B/16 / ImageNet-100
GPT-2 Medium / SST-2 (5 seeds, mean accuracy)
| Setting | Standard | SAL | Δ | SAL wins |
|---|---|---|---|---|
| dense | 90.05% | 90.62% | +0.57pp | 4/5 |
| INT8 | 88.88% | 89.61% | +0.73pp | 4/5 |
| INT4 (NF4) | 90.02% | 90.23% | +0.21pp | 3/5 |
| prune 33% heads | 85.71% | 88.17% | +2.45pp | 5/5 |
| prune 50% heads | 80.87% | 82.94% | +2.06pp | 4/5 |
| prune 33% + INT8 | 82.84% | 84.72% | +1.88pp | 4/5 |
| prune 33% + INT4 | 85.67% | 87.25% | +1.58pp | 5/5 |
Other results
| Model | Result | Seeds |
|---|---|---|
| I-JEPA ViT-H/14 (631M) | SAL wins 35/36 comparisons at 33/50% head pruning (+0.4 to +1.9pp); no effect at 70% | 3 |
| ViT-B/16 SAL+QAT | +22pp mean at 50% pruning + INT4 | 3 |
Links
- 📦 sal-torch on PyPI —
pip install sal-torch - 🔧 GitHub
- 🌐 cognitive-engineering.dev
- 🛡️ sal-certify
- 📄 PPAM 2026, Springer LNCS (Paper CP031)
Data: CC BY 4.0. sal-torch: BSL 1.1. Built by Cognitive Engineering (Switzerland).
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