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.
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.
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.