Publications
Updated August 2026 — Ryan Research Institute
Research at Ryan Research Institute examines affective sovereignty, interpretive authority, emotion AI governance, narrative–affect geometry, human–AI interaction, and the institutional consequences of affective inference.
This page presents public research outputs with DOI, journal record, accepted article status, open data record, or public-facing coverage. Manuscripts under confidential peer review are not listed here by venue.
Accepted / Production
The Affective Thermodynamic Relationship: an empirical information-theoretic scaling relationship for normative-conflict collapse in large language models (2026)
Communications AI & Computing
Status: Accepted / in production with DOI
DOI: https://doi.org/10.1038/s44488-026-00006-y
→ Extends the machine-side collapse program into normative-conflict strain, treating interpretive failure as a measurable scaling problem in large language models.
Published Peer-Reviewed Articles
Algorithmic affective blunting quantifies the collapse curve of interpretative failure in large language models (2026)
Discover Artificial Intelligence
Status: Published
DOI: https://doi.org/10.1007/s44163-026-01573-w
→ Introduces Algorithmic Affective Blunting (AAB), a dose-dependent degradation of affective interpretive coherence in a large language model under semantic stress. In a standardized single-model setting, mean Affective Degradation Index rose from 0.16 in Control to 2.92 in Extreme exposure.
Visual citation card: Emotional AI Needs a Stress Test
Narrative–Affect Discrepancy as a Regulated Degree of Freedom in 351,734 Relationship Narratives (2026)
PLOS ONE, 21(5), e0348715
DOI: https://doi.org/10.1371/journal.pone.0348715
→ Models narrative–affect discrepancy as a regulated expressive degree of freedom and shows that an RLHF-aligned language model occupies an approximately 1.70× smaller expressive region than humans.
Media coverage and public writing:
PsyPost: A new study mapped 350,000 relationship stories and found a communication style AI struggles to copy
MIT Technology Review Korea: How Emotion AI Misses the Structure of Human Feeling
https://www.technologyreview.kr/기고-감정을-읽는-ai가-정작-놓치는-인간-감정의-구조/
Formal and Computational Foundations for Implementing Affective Sovereignty in Emotion AI Systems (2026)
Discover Artificial Intelligence
DOI: https://doi.org/10.1007/s44163-026-01000-0
→ Introduces Sovereign-by-Design, the DRIFT protocol, and auditable alignment metrics for protecting interpretive authority in emotion-aware AI systems.
Interrupting Resonant Amplification: A Mechanistic and Design Framework for Human–AI Interaction (2026)
Computers in Human Behavior Reports, 21, 100975
DOI: https://doi.org/10.1016/j.chbr.2026.100975
→ Defines feedback-driven escalation loops in human–AI interaction and proposes Cognitive Circuit Breakers for interrupting amplification without erasing user agency.
ANEST Narrative–Affect Dataset (ANAD v1): A Large-Scale Derived Feature Resource for Quantifying Narrative–Affective Discrepancy (2026)
Data in Brief
DOI: https://doi.org/10.1016/j.dib.2026.112643
→ Provides a large-scale derived feature resource for measuring narrative–affect discrepancy across 351,734 relationship narratives.
DefMoN: A Reproducible Framework for Theory-Grounded Synthetic Data Generation in Affective AI (2025)
Machine Learning with Applications, 23, 100817
DOI: https://doi.org/10.1016/j.mlwa.2025.100817
→ Introduces a reproducible framework for generating theory-grounded synthetic data around defensive motivational nodes and affective AI.
Public Preprints and Open Research Resources
Mental AI: Post-Deployment Criterion Endogeneity in the Causal Loop of Human Mental Life (2026)
Public preprint
DOI: https://doi.org/10.20944/preprints202607.2118.v1
→ Develops post-deployment criterion endogeneity as a problem in which AI inferences can alter the human states, evidence, and criteria later used to evaluate those same inferences.
From Composite Scores to Latent Variables: Reassessing Prospective Associations Between Social Substitution and Emotional Closeness in Human–AI Attachment (2026)
Corrected public research version · Zenodo v2.0
DOI: https://doi.org/10.5281/zenodo.21971714
→ Public corrected version. Study 4 uses a one-week interval; the prior directional claim is superseded, and the corrected analysis does not establish that social substitution precedes emotional closeness or the reverse.
Open Data and Research Resources
ANAD v1 / Narrative–Affect Discrepancy Dataset
Zenodo canonical record
DOI: https://doi.org/10.5281/zenodo.18680687
→ Open research resource supporting narrative–affect discrepancy measurement and derived-feature analysis.
Affective Sovereignty Research Resources
Implementation framework preprint:
https://doi.org/10.5281/zenodo.17154182
Minimal Declaration:
https://doi.org/10.5281/zenodo.16992479
→ Public research materials supporting the conceptual and design foundations of Affective Sovereignty.
NADI / ANEST Research Program Archives
Human baseline archive:
https://doi.org/10.5281/zenodo.17864574
ANEST theory archive:
https://doi.org/10.5281/zenodo.17864654
Narrative–affect geometry archive:
https://doi.org/10.5281/zenodo.17864774
→ Open archive records for the broader narrative–affect discrepancy and expressive-geometry research program.
Public Scholarship and Coverage
PsyPost
A new study mapped 350,000 relationship stories and found a communication style AI struggles to copy
MIT Technology Review Korea
Essays on emotion AI, interpretive authority, and affective sovereignty.
https://www.technologyreview.kr/author/ryankim/
AI Times Korea
AI 취약계층 정책의 빈칸… ‘해석을 되돌릴 권한’이 없다 — contributed policy essay, 11 August 2026.
→ Translates RRI’s interpretive-authority program into six operational principles for high-impact AI: purpose notice, uncertainty and alternatives, abstention, subject correction, correction propagation, and meaningful human review.
Public commentary and reported coverage on emotion AI, affective sovereignty, and the transfer of interpretive power.
Notes
This page separates published articles, accepted or in-production articles, open research resources, and public scholarship.
Manuscripts under confidential peer review are not listed here by journal venue until a public DOI, official record, accepted article status, or public archive record is available.
For research, reproducibility, validation, or theory-integration inquiries: