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=
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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.
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Sycophantic AI makes human interaction feel more effortful and less satisfying over time
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.