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mnemosyne-memory-lifecycle-benchmark

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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
$schema: string
$id: string
type: string
required: list<item: string>
  child 0, item: string
properties: struct<overall: struct<type: string>, by_category: struct<type: string>>
  child 0, overall: struct<type: string>
      child 0, type: string
  child 1, by_category: struct<type: string>
      child 0, type: string
scenario_id: string
to
{'scenario_id': 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 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 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              $schema: string
              $id: string
              type: string
              required: list<item: string>
                child 0, item: string
              properties: struct<overall: struct<type: string>, by_category: struct<type: string>>
                child 0, overall: struct<type: string>
                    child 0, type: string
                child 1, by_category: struct<type: string>
                    child 0, type: string
              scenario_id: string
              to
              {'scenario_id': Value('string')}
              because column names don't match

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Mnemosyne Memory Lifecycle Benchmark Dataset v0.2.0

This deterministic synthetic benchmark contains 40 scenarios across eight categories and five documented reference strategies. Strategy execution is blind to expected labels; execution and evaluation are separate phases.

Current results are fixture results, not real-world memory accuracy. Simple strategies remain competitive on straightforward current-state questions, while typed lineage uniquely reconstructs the historical point in the current fixtures. Twenty-five of 40 scenarios are currently non-discriminating and are preserved rather than hidden. Metrics are not_applicable when no valid denominator exists. Logical erasure is not physical deletion, and this benchmark does not establish superiority over production memory systems.

Public links:

Test Your Own Memory System

Tooling release v0.2.1 provides a provider-neutral, label-free subprocess protocol. Clone or download the package, copy an adapter template, implement JSON stdin/stdout, execute against the frozen scenarios, evaluate separately, build a result bundle, and load it into the Space locally. No result is uploaded automatically; there is no public leaderboard; users control whether they share anything. External outputs are community-provided and unverified.
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