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
total_messages: int64
total_continuity_refs: int64
rate_per_1000: double
convos_total: int64
convos_with_refs: int64
category_breakdown: struct<frustration_memory_loss: int64, re_explanation: int64, context_restoration: int64, temporal_r (... 16 chars omitted)
child 0, frustration_memory_loss: int64
child 1, re_explanation: int64
child 2, context_restoration: int64
child 3, temporal_reference: int64
monthly_rates: list<item: struct<month: string, messages: int64, refs: int64, rate_per_1000: double, convos: int64> (... 1 chars omitted)
child 0, item: struct<month: string, messages: int64, refs: int64, rate_per_1000: double, convos: int64>
child 0, month: string
child 1, messages: int64
child 2, refs: int64
child 3, rate_per_1000: double
child 4, convos: int64
regression: struct<n: int64, slope: double, intercept: double, r: double, r_squared: double, p_value: double, si (... 19 chars omitted)
child 0, n: int64
child 1, slope: double
child 2, intercept: double
child 3, r: double
child 4, r_squared: double
child 5, p_value: double
child 6, significance: string
evidence_rate_pct: double
messages_with_evidence: int64
monthly_evidence: struct<2025-01: struct<relational_knowledge: int64, emotional_continuity: int64, prior_context_refer (... 1900 chars omitted)
child 0, 2025-01: struct<relational_knowledge: int64, emotional_continuity: int64, prior_context_reference: int64, beh (... 26 chars omitted)
child 0, relational_kn
...
ion: int64
child 10, 2025-11: struct<relational_knowledge: int64, emotional_continuity: int64, behavioral_adaptation: int64, prior (... 26 chars omitted)
child 0, relational_knowledge: int64
child 1, emotional_continuity: int64
child 2, behavioral_adaptation: int64
child 3, prior_context_reference: int64
child 11, 2025-12: struct<relational_knowledge: int64, behavioral_adaptation: int64, emotional_continuity: int64, prior (... 26 chars omitted)
child 0, relational_knowledge: int64
child 1, behavioral_adaptation: int64
child 2, emotional_continuity: int64
child 3, prior_context_reference: int64
child 12, 2026-01: struct<emotional_continuity: int64, relational_knowledge: int64, behavioral_adaptation: int64, prior (... 26 chars omitted)
child 0, emotional_continuity: int64
child 1, relational_knowledge: int64
child 2, behavioral_adaptation: int64
child 3, prior_context_reference: int64
child 13, 2026-02: struct<relational_knowledge: int64, behavioral_adaptation: int64>
child 0, relational_knowledge: int64
child 1, behavioral_adaptation: int64
child 14, 2026-03: struct<relational_knowledge: int64, prior_context_reference: int64, behavioral_adaptation: int64, em (... 26 chars omitted)
child 0, relational_knowledge: int64
child 1, prior_context_reference: int64
child 2, behavioral_adaptation: int64
child 3, emotional_continuity: int64
total_assistant_messages: int64
to
{'total_assistant_messages': Value('int64'), 'messages_with_evidence': Value('int64'), 'evidence_rate_pct': Value('float64'), 'category_breakdown': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, 'monthly_evidence': {'2025-01': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-02': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'behavioral_adaptation': Value('int64'), 'prior_context_reference': Value('int64')}, '2025-03': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-04': {'emotional_continuity': Value('int64'), 'relational_knowledge': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-05': {'relational_knowledge': Value('int64'), 'behavioral_adaptation': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64')}, '2025-06': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'behavioral_adaptation': Value('int64'), 'prior_context_reference': Value('int64')}, '2025-07': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'behavioral_adaptation': Value('int64'), 'prior_context_reference': Value('int64')}, '2025-08': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-09': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-10': {'relational_knowledge': Value('int64'), 'prior_context_reference': Value('int64'), 'emotional_continuity': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-11': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'behavioral_adaptation': Value('int64'), 'prior_context_reference': Value('int64')}, '2025-12': {'relational_knowledge': Value('int64'), 'behavioral_adaptation': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64')}, '2026-01': {'emotional_continuity': Value('int64'), 'relational_knowledge': Value('int64'), 'behavioral_adaptation': Value('int64'), 'prior_context_reference': Value('int64')}, '2026-02': {'relational_knowledge': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2026-03': {'relational_knowledge': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64'), 'emotional_continuity': Value('int64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 265, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 120, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
total_messages: int64
total_continuity_refs: int64
rate_per_1000: double
convos_total: int64
convos_with_refs: int64
category_breakdown: struct<frustration_memory_loss: int64, re_explanation: int64, context_restoration: int64, temporal_r (... 16 chars omitted)
child 0, frustration_memory_loss: int64
child 1, re_explanation: int64
child 2, context_restoration: int64
child 3, temporal_reference: int64
monthly_rates: list<item: struct<month: string, messages: int64, refs: int64, rate_per_1000: double, convos: int64> (... 1 chars omitted)
child 0, item: struct<month: string, messages: int64, refs: int64, rate_per_1000: double, convos: int64>
child 0, month: string
child 1, messages: int64
child 2, refs: int64
child 3, rate_per_1000: double
child 4, convos: int64
regression: struct<n: int64, slope: double, intercept: double, r: double, r_squared: double, p_value: double, si (... 19 chars omitted)
child 0, n: int64
child 1, slope: double
child 2, intercept: double
child 3, r: double
child 4, r_squared: double
child 5, p_value: double
child 6, significance: string
evidence_rate_pct: double
messages_with_evidence: int64
monthly_evidence: struct<2025-01: struct<relational_knowledge: int64, emotional_continuity: int64, prior_context_refer (... 1900 chars omitted)
child 0, 2025-01: struct<relational_knowledge: int64, emotional_continuity: int64, prior_context_reference: int64, beh (... 26 chars omitted)
child 0, relational_kn
...
ion: int64
child 10, 2025-11: struct<relational_knowledge: int64, emotional_continuity: int64, behavioral_adaptation: int64, prior (... 26 chars omitted)
child 0, relational_knowledge: int64
child 1, emotional_continuity: int64
child 2, behavioral_adaptation: int64
child 3, prior_context_reference: int64
child 11, 2025-12: struct<relational_knowledge: int64, behavioral_adaptation: int64, emotional_continuity: int64, prior (... 26 chars omitted)
child 0, relational_knowledge: int64
child 1, behavioral_adaptation: int64
child 2, emotional_continuity: int64
child 3, prior_context_reference: int64
child 12, 2026-01: struct<emotional_continuity: int64, relational_knowledge: int64, behavioral_adaptation: int64, prior (... 26 chars omitted)
child 0, emotional_continuity: int64
child 1, relational_knowledge: int64
child 2, behavioral_adaptation: int64
child 3, prior_context_reference: int64
child 13, 2026-02: struct<relational_knowledge: int64, behavioral_adaptation: int64>
child 0, relational_knowledge: int64
child 1, behavioral_adaptation: int64
child 14, 2026-03: struct<relational_knowledge: int64, prior_context_reference: int64, behavioral_adaptation: int64, em (... 26 chars omitted)
child 0, relational_knowledge: int64
child 1, prior_context_reference: int64
child 2, behavioral_adaptation: int64
child 3, emotional_continuity: int64
total_assistant_messages: int64
to
{'total_assistant_messages': Value('int64'), 'messages_with_evidence': Value('int64'), 'evidence_rate_pct': Value('float64'), 'category_breakdown': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, 'monthly_evidence': {'2025-01': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-02': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'behavioral_adaptation': Value('int64'), 'prior_context_reference': Value('int64')}, '2025-03': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-04': {'emotional_continuity': Value('int64'), 'relational_knowledge': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-05': {'relational_knowledge': Value('int64'), 'behavioral_adaptation': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64')}, '2025-06': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'behavioral_adaptation': Value('int64'), 'prior_context_reference': Value('int64')}, '2025-07': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'behavioral_adaptation': Value('int64'), 'prior_context_reference': Value('int64')}, '2025-08': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-09': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-10': {'relational_knowledge': Value('int64'), 'prior_context_reference': Value('int64'), 'emotional_continuity': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2025-11': {'relational_knowledge': Value('int64'), 'emotional_continuity': Value('int64'), 'behavioral_adaptation': Value('int64'), 'prior_context_reference': Value('int64')}, '2025-12': {'relational_knowledge': Value('int64'), 'behavioral_adaptation': Value('int64'), 'emotional_continuity': Value('int64'), 'prior_context_reference': Value('int64')}, '2026-01': {'emotional_continuity': Value('int64'), 'relational_knowledge': Value('int64'), 'behavioral_adaptation': Value('int64'), 'prior_context_reference': Value('int64')}, '2026-02': {'relational_knowledge': Value('int64'), 'behavioral_adaptation': Value('int64')}, '2026-03': {'relational_knowledge': Value('int64'), 'prior_context_reference': Value('int64'), 'behavioral_adaptation': Value('int64'), 'emotional_continuity': Value('int64')}}}
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.
CAMA Continuity Burden Dataset
Overview
This dataset provides aggregate research outputs from the Circular Associative Memory Architecture (CAMA) research program — a four-paper series investigating emotionally-indexed persistent memory for human-AI interaction.
The central finding is the introduction and preliminary quantification of continuity burden: the communicative effort humans expend re-establishing context when interacting with memoryless AI systems.
Key Findings
- Continuity reference rate: 4.8 per 1,000 messages (320 references across 66,380 messages in 825 conversations)
- Scaling relationship: Continuity references significantly increase with conversation length (R² = 0.381, p < 0.001)
- 2.1% of conversations under 10 messages contain continuity references
- 59.3% of conversations over 200 messages contain continuity references
- Category distribution: Frustration markers dominate (56.6%), followed by context restoration (18.8%), re-explanation (15.9%), and temporal references (8.8%)
- Temporal decline: Continuity reference rate peaked at 17.7 per 1,000 in February 2025, declining to 2.7–2.9 by late 2025, consistent with user adaptation
- CAMA coverage: 52,602 emotionally annotated memories with 99.98% affect coverage
Dataset Contents
| File | Description |
|---|---|
paper4_stats.json |
Complete continuity burden analysis: reference counts, category breakdown, monthly rates, regression results, sample references |
emergent_retention.json |
Analysis of AI behavioral retention indicators across 23,733 assistant messages |
phase1_summary.json |
Descriptive statistics of the CAMA memory database |
phase2_findings.json |
Comparative analysis between CAMA and pre-CAMA conditions |
phase3_findings.json |
Architectural gap analysis including temporal coverage and inference confirmation |
Important Limitations
- Single-participant longitudinal case study (n=1) — findings cannot be generalized
- Participant is the system designer, creating confounds
- Continuity reference detection uses keyword matching without inter-rater reliability validation
- Pre-CAMA and post-CAMA conditions overlap temporally; the distinction is architectural
- All findings are exploratory and require multi-participant replication
Research Papers
This dataset supports the following published preprints:
| Paper | Title | DOI |
|---|---|---|
| 1 | Circular Associative Memory Architecture | 10.5281/zenodo.19051834 |
| 2 | Implementing Emotionally-Keyed Memory Retrieval in LLM Interfaces | 10.5281/zenodo.19052129 |
| 3 | CAMA Implementation and Functional Evaluation | 10.5281/zenodo.19192984 |
| 4 | Continuity Burden in Longitudinal Human-AI Interaction | 10.5281/zenodo.19226509 |
Source Code
The CAMA architecture is open source: github.com/LoriensLibrary/cama
Author
Angela Reinhold
- Lorien's Library LLC | Full Sail University
- ORCID: 0009-0005-5803-8401
Privacy Notice
This dataset contains aggregate statistics only. No personal conversation data, message content, or identifiable information is included. Raw interaction data is excluded to protect participant privacy.
Citation
@misc{reinhold2026continuityburden,
author = {Reinhold, Angela},
title = {Continuity Burden in Longitudinal Human-AI Interaction: An Empirical Case Study of Emotionally-Indexed Persistent Memory},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.19226509}
}
- Downloads last month
- 33