Datasets:

KikoCis
/

jspace-reproduction-qwen2.5-0.5b

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
model: string
layers: int64
hidden: int64
n_probes: int64
subspace_k: int64
ablation_layer: int64
mean_base_nll: double
mean_jspace_ablated_nll: double
mean_random_ablated_nll: double
jspace_damage: double
random_damage: double
causal_necessity_nats: double
mean_effrank_frac: double
mean_energy_top8: double
mean_per_layer_influence: list<item: double>
  child 0, item: double
jspace_ablated_nll: double
random_ablated_nll: double
per_layer_influence: list<item: double>
  child 0, item: double
energy_top8: double
prompt: string
effrank_frac: double
workspace_concepts_by_depth: struct<0: list<item: string>, 4: list<item: string>, 8: list<item: string>, 12: list<item: string>,  (... 71 chars omitted)
  child 0, 0: list<item: string>
      child 0, item: string
  child 1, 4: list<item: string>
      child 0, item: string
  child 2, 8: list<item: string>
      child 0, item: string
  child 3, 12: list<item: string>
      child 0, item: string
  child 4, 16: list<item: string>
      child 0, item: string
  child 5, 20: list<item: string>
      child 0, item: string
  child 6, 24: list<item: string>
      child 0, item: string
base_nll: double
to
{'prompt': Value('string'), 'base_nll': Value('float64'), 'jspace_ablated_nll': Value('float64'), 'random_ablated_nll': Value('float64'), 'effrank_frac': Value('float64'), 'energy_top8': Value('float64'), 'per_layer_influence': List(Value('float64')), 'workspace_concepts_by_depth': {'0': List(Value('string')), '4': List(Value('string')), '8': List(Value('string')), '12': List(Value('string')), '16': List(Value('string')), '20': List(Value('string')), '24': List(Value('string'))}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 478, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              model: string
              layers: int64
              hidden: int64
              n_probes: int64
              subspace_k: int64
              ablation_layer: int64
              mean_base_nll: double
              mean_jspace_ablated_nll: double
              mean_random_ablated_nll: double
              jspace_damage: double
              random_damage: double
              causal_necessity_nats: double
              mean_effrank_frac: double
              mean_energy_top8: double
              mean_per_layer_influence: list<item: double>
                child 0, item: double
              jspace_ablated_nll: double
              random_ablated_nll: double
              per_layer_influence: list<item: double>
                child 0, item: double
              energy_top8: double
              prompt: string
              effrank_frac: double
              workspace_concepts_by_depth: struct<0: list<item: string>, 4: list<item: string>, 8: list<item: string>, 12: list<item: string>,  (... 71 chars omitted)
                child 0, 0: list<item: string>
                    child 0, item: string
                child 1, 4: list<item: string>
                    child 0, item: string
                child 2, 8: list<item: string>
                    child 0, item: string
                child 3, 12: list<item: string>
                    child 0, item: string
                child 4, 16: list<item: string>
                    child 0, item: string
                child 5, 20: list<item: string>
                    child 0, item: string
                child 6, 24: list<item: string>
                    child 0, item: string
              base_nll: double
              to
              {'prompt': Value('string'), 'base_nll': Value('float64'), 'jspace_ablated_nll': Value('float64'), 'random_ablated_nll': Value('float64'), 'effrank_frac': Value('float64'), 'energy_top8': Value('float64'), 'per_layer_influence': List(Value('float64')), 'workspace_concepts_by_depth': {'0': List(Value('string')), '4': List(Value('string')), '8': List(Value('string')), '12': List(Value('string')), '16': List(Value('string')), '20': List(Value('string')), '24': List(Value('string'))}}
              because column names don't match

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J-space Reproduction on Qwen2.5-0.5B

Per-probe measurements reproducing Anthropic's "global workspace" / J-space finding on a small open model (Qwen2.5-0.5B-Instruct, 24 layers, 896 hidden dims), computed on CPU.

This is a reproduction-measurements dataset, not a training dataset. It exists so anyone can check the central claim: a language model does part of its reasoning in a tiny, causally-necessary subspace of its activations, separate from the tokens it emits.

📄 Original research: Anthropic, A global workspace in language models · full write-up
✍️ The story of this reproduction: bitácora post — "The tiny workspace where a small model does its thinking"

The headline result (12 reasoning probes, mean)

quantity	value
reasoning workspace size (effective rank)	1.5% of hidden dims
future-influence energy in the top-8 SVD directions	76%
reasoning error, model intact	2.83 nats
reasoning error, random 8-dim slice removed	3.45 nats (+0.62)
reasoning error, J-space 8-dim subspace removed	4.07 nats (+1.24)
causal necessity (extra damage from removing the J-space vs a random slice)	+0.62 nats (≈2× the damage)

Removing the 8-dimensional J-space hurts reasoning roughly twice as much as removing a random slice of the same size — the model genuinely leans on that tiny subspace.

How it was measured (the Jacobian lens)

Future-influence Jacobian — back-propagate the loss on the model's own continuation to each layer's activations: G_l = d(next-token NLL) / d(hidden_l). Each row is the direction that activation pushes future outputs.
Subspace extraction — SVD of G_l; keep the top-8 right-singular directions = the J-space at that layer. Report its effective-rank fraction and top-8 energy.
Causal ablation — project the J-space out of the residual stream mid-network and measure the reasoning-NLL increase, against a random equal-rank subspace control.

Full method code is included (jspace_advanced.py, jspace_report.py); it runs on CPU in a couple of minutes. python jspace_report.py regenerates jspace_measurements.jsonl + jspace_summary.json.

Files

jspace_measurements.jsonl — one row per probe:
- prompt, base_nll, jspace_ablated_nll, random_ablated_nll
- effrank_frac, energy_top8 (subspace stats)
- per_layer_influence — future-influence norm per layer (24 values)
- workspace_concepts_by_depth — logit-lens top tokens at the final position, by depth
jspace_summary.json — the aggregate table above
jspace_advanced.py, jspace_report.py — the exact measurement code

Honest caveats

One small model, one subspace size, twelve prompts. The causal claim (a tiny, load-bearing reasoning subspace exists) replicates cleanly and robustly. The content is hard to read on a 0.5B model — the logit-lens workspace concepts are noisy and only sharpen in the last layers.
The original paper's "planning" aspect (future-relevant concepts activating early) is weak here: on this model reasoning mostly resolves in the last few layers.
The J-space is a diagnostic lens, not an engineering knob — attempts to exploit it (e.g. mixed- precision quantization or as a data-free model-merge fitness) do not work; that's documented separately.

Citation

If Anthropic's finding is what you're citing, cite their paper. If you use these reproduction measurements or the method code, a link back to the bitácora post is appreciated.

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