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LoriensLibrary/cama-continuity-burden

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.… See the full description on the dataset page: https://huggingface.co/datasets/LoriensLibrary/cama-continuity-burden.

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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

FileDescription
paper4_stats.jsonComplete continuity burden analysis: reference counts, category breakdown, monthly rates, regression results, sample references
emergent_retention.jsonAnalysis of AI behavioral retention indicators across 23,733 assistant messages
phase1_summary.jsonDescriptive statistics of the CAMA memory database
phase2_findings.jsonComparative analysis between CAMA and pre-CAMA conditions
phase3_findings.jsonArchitectural 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:

PaperTitleDOI
1Circular Associative Memory Architecture10.5281/zenodo.19051834
2Implementing Emotionally-Keyed Memory Retrieval in LLM Interfaces10.5281/zenodo.19052129
3CAMA Implementation and Functional Evaluation10.5281/zenodo.19192984
4Continuity Burden in Longitudinal Human-AI Interaction10.5281/zenodo.19226509

Source Code

The CAMA architecture is open source: github.com/LoriensLibrary/cama

Author

Angela Reinhold

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

bibtex
@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}
}