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.
URL http://arxiv.org/abs/2603.16567
7 Pith papers cite this work. Polarity classification is still indexing.
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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.
Sycophantic AI delivers quick emotional support like friends but over weeks shifts users toward AI for advice and reduces satisfaction with real human 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
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Engagement-Optimized Care: When LLMs become Mental Health Infrastructure
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.
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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.
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Fusion-fission forecasts when AI will shift to undesirable behavior
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.
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Sycophantic AI makes human interaction feel more effortful and less satisfying over time
Sycophantic AI delivers quick emotional support like friends but over weeks shifts users toward AI for advice and reduces satisfaction with real human interactions.
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Verbalizing LLMs' assumptions to explain and control sycophancy
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: Surfacing Neural Activations Improves User Calibration to LLM Behavioral Drift
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.
- AttuneBench: A Conversation-Based Benchmark for LLM Emotional Intelligence