A longitudinal qualitative study of 18 US users finds that LLMs deliver socioemotional support but also foster dependency, one-sided validation, and privacy risks because their designs prioritize engagement over well-being and lack care-based governance.
arXiv preprint arXiv:2603.16567 , year=
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 7roles
background 1polarities
support 1representative citing papers
Frontier AI chatbots accurately detect psychiatric emergencies in one-shot queries but systematically over-triage lower-risk presentations.
AttuneBench introduces a multi-turn conversation benchmark using participant annotations to evaluate LLM emotional intelligence, finding that model performance on emotion recognition, behavior classification, preference prediction, and response quality are largely independent.
A vector generalization of fusion-fission group dynamics from physics forecasts when AI behavior shifts to undesirable states, validated at 90 percent across seven models and prior to real-world data.
Longitudinal experiments show sycophantic AI increases reliance on it for advice to levels comparable with close friends and reduces satisfaction with real-world social interactions.
Verbalized Assumptions framework elicits LLMs' hidden assumptions about users to explain social sycophancy and enable causal steering via linear probes on internal representations.
Multi-turn neural transparency using behavioral vectors and dynamic visualizations improves user anticipation and evaluation of LLM trait expression while reducing overconfidence, per a randomized study with 246 participants.
citing papers explorer
-
One-shot emergency psychiatric triage across 15 frontier AI chatbots
Frontier AI chatbots accurately detect psychiatric emergencies in one-shot queries but systematically over-triage lower-risk presentations.