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: TypeError
Message: Couldn't cast array of type
struct<base: double, addressable_pct: double, no_foothold_pct: double, oracle: struct<gamma: double, r5: double, gain: double>, real: struct<m: int64, gamma: double, r5: double, gain: double>>
to
{'addressable': Value('int64'), 'bridged_pct': Value('float64'), 'title_bridged_pct': Value('float64'), 'bridge_df_median': Value('float64'), 'lift': {'2': Value('null'), '5': Value('float64'), '10': Value('float64'), '50': Value('float64'), '1000000000': Value('float64')}}
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 489, 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 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<base: double, addressable_pct: double, no_foothold_pct: double, oracle: struct<gamma: double, r5: double, gain: double>, real: struct<m: int64, gamma: double, r5: double, gain: double>>
to
{'addressable': Value('int64'), 'bridged_pct': Value('float64'), 'title_bridged_pct': Value('float64'), 'bridge_df_median': Value('float64'), 'lift': {'2': Value('null'), '5': Value('float64'), '10': Value('float64'), '50': Value('float64'), '1000000000': Value('float64')}}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
NeuroGraphDB — experiment results
Raw result files for every experiment in maninbook/NeuroGraphDB, a study of graph retrieval for multi-hop QA that builds its graph without any LLM calls.
Each JSON holds the aggregate metrics, the McNemar test, and the run configuration (pool size, seed, embedder, LLM, hyperparameters) for one run. Every number in the repository README traces to a file here.
Contents
| prefix | experiment |
|---|---|
baseline_* |
dense / BM25 / RRF hybrid baselines |
graph_* |
seed-sparsity sweep — the core finding |
qa_*, compare3_* |
end-to-end QA, Qwen2.5-7B and 72B |
hipporag_* |
HippoRAG 2 run under our conditions, with its rankings |
gate_* |
query gating — replicated across three seeds |
hebbian_*, prop_*, alias_*, fan_* |
pre-registered mechanisms that were rejected |
rematch_* |
MuSiQue head-to-head with gating enabled |
The rejected experiments are kept deliberately. Seven of nine pre-registered mechanisms failed, and the records include the mechanism predictions written down before each run.
Headline
Retrieval, fraction of questions with all supporting passages in the top 10, n=500, identical pool / questions / embedder / LLM:
| dense | ours | HippoRAG 2 | |
|---|---|---|---|
| HotpotQA | 0.874 | 0.940 | 0.934 |
| 2WikiMultihopQA | 0.534 | 0.920 | 0.796 |
| MuSiQue | 0.330 | 0.394 | 0.454 |
Indexing 4,943 passages: 74 minutes (HippoRAG 2, 2 LLM calls per passage) versus seconds (this method, zero LLM calls).
We do not claim higher answer accuracy — in end-to-end QA we never beat HippoRAG 2 significantly, and it beats us on MuSiQue.
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