BASALITH · XYZ
RESEARCH INDEX

Cited Research

Nine papers underpin the Basalith architecture. Each citation in the white paper links to its full entry here.

9
PAPERS
5
RESEARCH AREAS
2021-2025
PUBLICATION RANGE
4
PEER-REVIEWED
[1]
THEORETICAL FOUNDATION1966§01§11

The Tacit Dimension

Polanyi, M. · University of Chicago Press

Tacit knowledge exists only in practice. It cannot be fully articulated in words.

[2]
KNOWLEDGE TRANSFER2024§01§10§11

Procedures for transferring organizational knowledge during generational change: A systematic review

Igoa-Iraola, E., & Diez, F. · Heliyon, 10(4)

68% of organizations attempt tacit knowledge transfer, but effective methods remain elusive.

[3]
KNOWLEDGE TRANSFER2025§01§11

Knowledge Transfer and Succession Planning Certificate

University of Vermont citing Harvard Business Review research · University of Vermont Continuing Education

Poor succession planning costs organizations $1 trillion in lost market value annually.

[4]
USER RESEARCH2025§01§11

AI Afterlife as Digital Legacy: Perceptions, Expectations, and Concerns

Lei, Y. et al. · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

Intentional participation distinguishes authentic digital legacy from reactive reconstruction.

[5]
COGNITIVE FINGERPRINTING2021§02§11

A cognitive fingerprint in human random number generation

Schulz, M-A., Baier, S., Timmermann, B., Bzdok, D., & Witt, K. · Scientific Reports, 11

Individual behavioral patterns in random number generation are measurably distinctive at 96.5% AUC — establishing that cognitive signatures differ meaningfully between individuals.

[6]
LLM RESEARCH2025§05§11

Large Language Models for Psychological Assessment: A Comprehensive Overview

Brickman, J., Gupta, M., & Oltmanns, J.R. · Advances in Methods and Practices in Psychological Science

Fine-tuned LLMs can predict personality traits from social media posts with meaningful accuracy.

[7]
PRIVACY RISK2025§09§11

Unintended Memorization of Sensitive Information in Fine-Tuned Language Models

Anonymous · arXiv:2601.17480

LLMs memorize training samples even when seen once, requiring curated training pipelines.

[8]
PLLM TAXONOMY2025§05§11

A Survey of Personalized Large Language Models: Progress and Future Directions

Au, S. et al. · arXiv:2502.11528

Per-user PEFT provides a viable paradigm for personalized behavioral fine-tuning at scale.

[9]
ORAL HISTORY2025§11

Large Language Models for Oral History Understanding with Text Classification and Sentiment Analysis

Anonymous · arXiv:2508.06729

LLMs effectively annotate 92,191 sentences from 1,002 oral history interviews.