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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'llm_judge', 'metrics'}) and 1 missing columns ({'answer_evidences'}).
This happened while the json dataset builder was generating data using
hf://datasets/redbearai/MemoryBear_eval_result/lme/memorybear/memorybear_lme_judged.json (at revision 84d77cda89e9b730c6f1ba20d753e37ebd886465), ['hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_retrieved_memories.json'], ['hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_retrieved_memories.json']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._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
question_id: string
question: string
question_type: string
question_date: string
golden_answer: extension<arrow.json>
hypothesis: string
llm_judge: struct<correct: bool>
child 0, correct: bool
metrics: struct<context_tokens: int64, lexical: struct<f1: double, rouge1_f: double, rouge2_f: double, rougeL (... 88 chars omitted)
child 0, context_tokens: int64
child 1, lexical: struct<f1: double, rouge1_f: double, rouge2_f: double, rougeL_f: double, bleu1: double, bleu2: doubl (... 48 chars omitted)
child 0, f1: double
child 1, rouge1_f: double
child 2, rouge2_f: double
child 3, rougeL_f: double
child 4, bleu1: double
child 5, bleu2: double
child 6, bleu3: double
child 7, bleu4: double
child 8, meteor: double
latency_ms: double
retrieved_memories: null
-- schema metadata --
huggingface: '{"info": {"features": {"question_id": {"dtype": "string", "' + 977
to
{'question_id': Value('string'), 'question': Value('string'), 'question_type': Value('string'), 'question_date': Value('string'), 'golden_answer': Json(decode=True), 'answer_evidences': List(Value('string')), 'retrieved_memories': {'answer': Value('string'), 'intermediate_outputs': List({'source': Value('string'), 'score': Value('float64'), 'content': Value('string'), 'data': Json(decode=True), 'query': Value('string'), 'id': Value('string')})}, 'hypothesis': Value('string'), 'latency_ms': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'llm_judge', 'metrics'}) and 1 missing columns ({'answer_evidences'}).
This happened while the json dataset builder was generating data using
hf://datasets/redbearai/MemoryBear_eval_result/lme/memorybear/memorybear_lme_judged.json (at revision 84d77cda89e9b730c6f1ba20d753e37ebd886465), ['hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_retrieved_memories.json'], ['hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memorybear/memorybear_lme_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/lme/memos/memos_lme_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/mem0/mem0_locomo_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memorybear/memorybear_locomo_retrieved_memories.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_hypotheses.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_judged.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_metrics.json', 'hf://datasets/redbearai/MemoryBear_eval_result@84d77cda89e9b730c6f1ba20d753e37ebd886465/locomo/memos/memos_locomo_retrieved_memories.json']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
question_id string | question string | question_type string | question_date string | golden_answer string | answer_evidences list | retrieved_memories dict | hypothesis string | latency_ms float64 |
|---|---|---|---|---|---|---|---|---|
gpt4_2655b836 | What was the first issue I had with my new car after its first service? | temporal-reasoning | 2023/04/10 (Mon) 23:07 | "GPS system not functioning correctly" | [
"user: I'm thinking of getting my car detailed soon. Do you know any good detailers in the area or have any recommendations? By the way, I just got my car serviced for the first time on March 15th, and it was a great experience.",
"user: I've been doing some research and found a local detailer with great reviews.... | {
"answer": "<user-info>\n<core-facts>['keeps track of gas mileage', 'current vehicle gets around 32 mpg', 'owns a silver Honda Civic', 'has a safe driving record']</core-facts>\n<events>['purchased silver Honda Civic | February 10, 2023', 'plans road trip | soon after April 10, 2023']</events>\n<goals>['plan to chec... | GPS system malfunction | 730 |
gpt4_2487a7cb | Which event did I attend first, the 'Effective Time Management' workshop or the 'Data Analysis using Python' webinar? | temporal-reasoning | 2023/05/28 (Sun) 06:47 | "'Data Analysis using Python' webinar" | [
"user: I've been thinking about my goals and priorities lately, and I realized that I want to focus more on learning new skills and expanding my knowledge in different areas. I've been attending various workshops and lectures, like the workshop on \"Effective Time Management\" at the local community center last Sat... | {
"answer": "<user-info>\n<beliefs-or-stances>['considers social media a major distraction for tasks requiring concentration', 'does not consider data connection and integration a major concern']</beliefs-or-stances>\n<core-facts>['working on a project involving data analysis']</core-facts>\n<events>['attended worksh... | The 'Data Analysis using Python' webinar | 500 |
gpt4_76048e76 | Which vehicle did I take care of first in February, the bike or the car? | temporal-reasoning | 2023/03/10 (Fri) 23:15 | "bike" | [
"user: I'm thinking of getting a new bike rack for my car. Do you have any recommendations? By the way, I've been having some issues with my bike lately - in mid-February, I had to take it in for repairs because the gears were acting up and I couldn't shift properly.",
"user: I'm thinking of getting a newer car a... | {
"answer": "<user-info>\n<core-facts>['owns a hybrid bike', 'owns a Toyota Corolla', 'relies on car for daily errands']</core-facts>\n<events>['took current car to dealership for routine checkup | March 10, 2023', 'detailing Corolla | February 27, 2023 | spent about $20', 'renewed car insurance with a different prov... | The bike (on February 15, 2023, before the car on February 27, 2023). | 550 |
gpt4_2312f94c | Which device did I got first, the Samsung Galaxy S22 or the Dell XPS 13? | temporal-reasoning | 2023/03/15 (Wed) 03:53 | "Samsung Galaxy S22" | [
"user: I'm planning a trip to Hawaii and I want to make sure my new phone stays charged. Can you recommend some must-visit places in Hawaii? By the way, I recently got a new Samsung Galaxy S22 from the Best Buy store at the mall on February 20th, and I'm loving it so far.",
"user: I'm planning a trip to Hawaii an... | {
"answer": "<user-info>\n<events>['purchased Samsung Galaxy S22 from Best Buy | February 20, 2023', 'purchased Anker PowerCore 20000 portable power bank from Amazon | February 13, 2023', 'plans to rent a car to drive the Road to Hana | 2023-03-15', 'pre-ordered Dell XPS 13 laptop | January 28, 2023']</events>\n<goal... | Samsung Galaxy S22 | 600 |
0bb5a684 | How many days before the team meeting I was preparing for did I attend the workshop on 'Effective Communication in the Workplace'? | temporal-reasoning | 2023/01/13 (Fri) 19:39 | "7 days. 8 days (including the last day) is also acceptable." | [
"user: I'm preparing for an upcoming meeting with my team and I want to make sure I'm effectively communicating our project updates. I recently attended a workshop on \"Effective Communication in the Workplace\" on January 10th, which was really helpful in improving my active listening and conflict resolution skill... | {
"answer": "<user-info>\n<beliefs-or-stances>['considers self-awareness a key takeaway in workplace communication']</beliefs-or-stances>\n<events>['attended workshop on \"Effective Communication in the Workplace\" | January 10, 2023 | improved active listening and conflict resolution skills']</events>\n<goals>['impr... | 7 days | 440 |
08f4fc43 | How many days had passed between the Sunday mass at St. Mary's Church and the Ash Wednesday service at the cathedral? | temporal-reasoning | 2023/02/20 (Mon) 22:35 | "30 days. 31 days (including the last day) is also acceptable." | [
"user: I'm planning to volunteer at a local soup kitchen this weekend and I was wondering if you could give me some tips on how to make a positive impact during my time there. By the way, I just came from the Ash Wednesday service at the cathedral on February 1st, and it really made me reflect on the importance of ... | {
"answer": "<user-info>\n<beliefs-or-stances>['values giving back to the community', 'believes service projects strengthen relationships among group members and with God']</beliefs-or-stances>\n<events>['attended Ash Wednesday service | 1 February 2023 | reflected on giving back to community', \"attended Sunday mass... | 30 days | 520 |
2c63a862 | How many days did it take for me to find a house I loved after starting to work with Rachel? | temporal-reasoning | 2022/03/02 (Wed) 11:02 | "14 days. 15 days (including the last day) is also acceptable." | [
"user: I'd like to ask Rachel about the new construction listings in these areas. Since I started working with her on 2/15, I'm hoping she can give me a better sense of what's available in my budget. Do you think I should also ask her about any upcoming developments or projects in the pipeline?",
"user: I'm looki... | {
"answer": "<user-info>\n<core-facts>['works in Irvine, California']</core-facts>\n<events>['pre-approved for a mortgage up to $350,000 | March 2, 2022', 'started working with Rachel | February 15, 2022', 'saw a house they really loved | March 1, 2022', 'considered making an offer of around $320,000 on a house liste... | 14 days | 540 |
gpt4_385a5000 | Which seeds were started first, the tomatoes or the marigolds? | temporal-reasoning | 2023/03/10 (Fri) 02:46 | "Tomatoes" | [
"user: I'm planning to plant some seedlings in my backyard soon. Can you tell me what's the ideal temperature for planting them outside? By the way, I just started some marigold seeds that arrived on March 3rd, and they're already germinating - excited to see them grow!",
"user: I'm planning to plant out my seedl... | {
"answer": "<user-info>\n<core-facts>['lives in 23456 zip code']</core-facts>\n<events>['plans to transplant seedlings including marigolds into larger pots | soon after 10 March 2023', 'plans to add support for marigolds | 10 March 2023 | as they grow', 'started seeds indoors under grow lights in the basement | 20 F... | Tomatoes | 470 |
2a1811e2 | How many days had passed between the Hindu festival of Holi and the Sunday mass at St. Mary's Church? | temporal-reasoning | 2023/03/26 (Sun) 21:19 | "21 days. 22 days (including the last day) is also acceptable." | [
"user: I'm planning to volunteer at a local community event next weekend and I was wondering if you could suggest some ideas for fundraising activities that have been successful in the past. By the way, I just got back from Sunday mass at St. Mary's Church on March 19th, where Father John's sermon really inspired m... | {
"answer": "<user-info>\n<events>[\"attended Sunday mass at St. Mary's Church | March 19, 2023\", 'plans to volunteer at local community event | April 1 to April 2, 2023', 'seeks tips to promote charity event on social media | 2023-03-26', 'attended Holi celebration at local temple | February 26, 2023 | enjoyed thro... | 21 days | 430 |
bbf86515 | How many days before the 'Rack Fest' did I participate in the 'Turbocharged Tuesdays' event? | temporal-reasoning | 2023/06/28 (Wed) 20:07 | "4 days." | [
"user: I'm looking for some recommendations on performance air filters for my 2018 Ford Mustang GT. I recently modified my exhaust system and I'm looking to squeeze out some more power. By the way, I just participated in the \"Turbocharged Tuesdays\" auto racking event at the local racing track on June 14th, where ... | {
"answer": "<user-info>\n<core-facts>['owns a 2018 Ford Mustang GT']</core-facts>\n<events>['attended \"Turbocharged Tuesdays\" event | 14 June 2023 | received free NitroFuel fuel additive', 'reserved spot to participate in \"Racing Nationals\" drag racing event | August 2023', 'achieved personal best quarter-mile t... | 4 days | 510 |
gpt4_5dcc0aab | Which pair of shoes did I clean last month? | temporal-reasoning | 2023/05/30 (Tue) 01:50 | "white Adidas sneakers" | [
"user: I'm glad I finally got around to cleaning my white Adidas sneakers last month, which I'd been meaning to do for weeks. They were getting pretty dirty after that outdoor music festival I attended."
] | {
"answer": "<user-info>\n<beliefs-or-stances>['ankle support is important for rough mountain trails', 'considers breathability important', 'prefers lightweight footwear for hiking']</beliefs-or-stances>\n<events>['attended outdoor music festival | 15 April 2023']</events>\n<goals>['hike in the mountains']</goals>\n<... | white Adidas sneakers | 840 |
gpt4_0b2f1d21 | Which event happened first, the purchase of the coffee maker or the malfunction of the stand mixer? | temporal-reasoning | 2023/05/22 (Mon) 18:10 | "The malfunction of the stand mixer" | [
"user: I'm having some issues with my coffee maker's performance lately. I bought it about three weeks ago, it's a black and stainless steel machine from a well-known brand, and I've been using it every morning since then. Do you have any cleaning or maintenance tips to keep it running smoothly?",
"user: I'm look... | {
"answer": "<user-info>\n<events>['stand mixer repair | April 2023 | baked by hand during two-week period', 'plan to organize a dinner party | soon after May 22, 2023']</events>\n<goals>['cook more at home']</goals>\n<interests>['baking cakes and cookies', 'prefers dessert recipes that do not require a stand mixer']... | The malfunction of the stand mixer | 510 |
f0853d11 | How many days had passed between the 'Walk for Hunger' event and the 'Coastal Cleanup' event? | temporal-reasoning | 2023/03/14 (Tue) 15:20 | "14 days. 8 days (including the last day) is also acceptable." | [
"user: I'm planning a beach trip to Santa Monica soon and I was wondering if you could recommend some eco-friendly sunscreen brands. By the way, I recently volunteered at the Coastal Cleanup event on March 7th and it was amazing to see the impact we made on keeping our beaches clean.",
"user: I'm looking to find ... | {
"answer": "<user-info>\n<events>['volunteered at Coastal Cleanup event | March 7, 2023 | found impact on keeping beaches clean amazing', 'participated in Walk for Hunger 5K walk | February 21, 2023 | had a great time']</events>\n<goals>['participate in charity walking or running events']</goals>\n<interests>['impac... | 14 days | 480 |
gpt4_6ed717ea | Which item did I purchase first, the dog bed for Max or the training pads for Luna? | temporal-reasoning | 2023/05/20 (Sat) 06:04 | "Training pads for Luna" | [
"user: I'm thinking of getting some more supplies for my puppy, Luna. She's still in potty-training, and I've been using those eco-friendly training pads from Chewy.com. I got a set of 10 for $25 about a month ago, and they've been a lifesaver. Do you have any recommendations for other potty-training aids or tips?"... | {
"answer": "<user-info>\n<events>['ordered 6-month supply of generic Frontline from Chewy.com | April 2023', 'purchased 30-pound bag of Acana dog food from PetSmart | before May 20, 2023', 'purchased pet-safe cleaning products from Amazon | before May 20, 2023', 'obtained pack of 20 dental chews for $15 | recently b... | The training pads for Luna | 640 |
gpt4_70e84552 | Which task did I complete first, fixing the fence or trimming the goats' hooves? | temporal-reasoning | 2023/05/22 (Mon) 13:04 | "Fixing the fence" | [
"user: I'm thinking of hosting a farm open house event soon and I want to make sure everything is perfect. Can you help me come up with some ideas for activities and also give me some tips on how to promote it on social media? By the way, I just fixed that broken fence on the east side of my property three weeks ag... | {
"answer": "<user-info>\n<core-facts>['owns goats and property']</core-facts>\n<events>['fixed broken fence on property | May 1, 2023', 'vaccinated goats against parasites | May 18, 2023', 'made delicious quiche with fresh herbs from garden | recently before May 22, 2023', 'researched online and consulted local farm... | Fixing the fence | 510 |
End of preview.
MemoryBear Evaluation Results
This dataset repository contains the evaluation results for MemoryBear, a next-generation AI memory system developed by RedBear AI.
MemoryBear's core breakthrough lies in moving beyond the limitations of traditional "static knowledge storage". Inspired by the cognitive mechanisms of biological brains, MemoryBear builds an intelligent knowledge-processing framework that spans the full lifecycle of perception → extraction → association → forgetting.
Unlike traditional memory tools that treat knowledge as static data to be retrieved, MemoryBear emulates the hippocampus's memory encoding, the neocortex's knowledge consolidation, and synaptic pruning-based forgetting — enabling knowledge to dynamically evolve with life-like properties. This shifts the relationship between AI and users from passive lookup to proactive cognitive assistance.
Benchmarks
We evaluate on two widely used long-term conversational memory benchmarks:
- LongMemEval (xiaowu0162/LongMemEval) — 500 questions probing five core long-term memory abilities of chat assistants (information extraction, multi-session reasoning, temporal reasoning, knowledge updates, and abstention) over long user–assistant interaction histories.
- LoCoMo (snap-research/locomo) — 1,986 questions over 10 very long multi-session dialogues, covering single-hop, multi-hop, temporal-reasoning, open-domain, and adversarial questions.
Repository Structure
The evaluation artifacts are organized by benchmark (lme/ for LongMemEval, locomo/ for LoCoMo), then by system. Each run directory contains the same five artifacts:
| File | Description |
|---|---|
*_metrics.json |
Aggregated metrics — accuracy, average context tokens, latency, and lexical scores (F1 / ROUGE / BLEU / METEOR), reported overall, by question category, and per question. |
*_hypotheses.json |
The answer generated by the system for each question, alongside the question, golden answer, and answer evidences. |
*_judged.json |
Per-question LLM-judge verdicts (correct / incorrect) together with the associated lexical metrics. |
*_retrieved_memories.json |
The memories retrieved by the system for each question, useful for inspecting retrieval quality. |
*_results.xlsx |
A spreadsheet summary of the run for convenient browsing. |
Baseline Reproduction
The baseline results were reproduced by us using the official reproduction repos.
For comparability, the baselines were run under the same settings as MemoryBear: retrieval returns the top 10 memories by default, and both hypothesis (answer) generation and LLM judging use qwen3.7-plus — identical to our own runs.
Evaluation Results
LongMemEval
Evaluated on the full 500-question LongMemEval set. Accuracy is determined by an LLM judge.
| System | single-session-preference | single-session-assistant | temporal-reasoning | multi-session | knowledge-update | single-session-user | overall |
|---|---|---|---|---|---|---|---|
| MemoryBear | 100% | 85.71% | 93.98% | 93.98% | 98.72% | 100% | 95.0% |
| MemOS | 86.67% | 92.86% | 81.95% | 80.45% | 94.87% | 98.57% | 87.4% |
| Memobase | 78.40% | 22.51% | 72.13% | 63.56% | 87.05% | 91.00% | 69.65% |
| Mem0 | 88.20% | 25.98% | 68.57% | 59.99% | 64.67% | 81.20% | 63.86% |
| Zep | 52.23% | 72.75% | 51.40% | 45.03% | 72.17% | 91.04% | 61.51% |
| Supermemory | 88.20% | 57.15% | 42.14% | 50.00% | 53.47% | 84.00% | 56.31% |
| MIRIX | 52.26% | 61.72% | 24.28% | 28.57% | 50.98% | 71.39% | 42.02% |
| MemU | 75.14% | 19.05% | 16.43% | 40.00% | 39.79% | 65.80% | 37.07% |
LoCoMo
Evaluated on the full LoCoMo benchmark (1,986 questions across 10 conversations). Accuracy is determined by an LLM judge. The rest of the system baselines cover the 1,540 non-adversarial questions, so their adversarial cells are empty and their overall scores are computed over the remaining four categories.
| System | single-hop | multi-hop | temporal-reasoning | open-domain | adversarial | overall | overall F1 |
|---|---|---|---|---|---|---|---|
| MemoryBear | 92.27% | 90.78% | 91.59% | 73.96% | 94.39% | 91.54% | 67.49 |
| MemOS | 89.89% | 77.30% | 81.93% | 63.54% | – | 84.29% | 38.44 |
| Mem0 | 80.98% | 84.40% | 88.16% | 73.96% | – | 82.66% | 48.74 |
| Memobase | 71.66% | 61.42% | 77.14% | 51.53% | – | 69.68% | 50.18 |
| MIRIX | 66.86% | 51.55% | 65.11% | 45.47% | – | 62.29% | 28.10 |
| Zep | 64.91% | 49.51% | 52.08% | 32.33% | – | 57.39% | 41.23 |
| MemU | 65.01% | 59.96% | 25.75% | 48.50% | – | 54.87% | 35.15 |
| Supermemory | 65.95% | 48.56% | 30.18% | 41.39% | – | 53.72% | 34.87 |
* The original LoCoMo dataset contains mislabeled golden answers. We corrected these mislabels, and all results above are reported on the corrected dataset.
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