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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LLMs for robotic health attendant control violate safety rules in 54.4% of harmful scenarios on average, with proprietary models at 23.7% median violation versus 72.8% for open-weight models, indicating they are not yet safe for clinical use.
citing papers explorer
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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.
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Benchmarking the Safety of Large Language Models for Robotic Health Attendant Control
LLMs for robotic health attendant control violate safety rules in 54.4% of harmful scenarios on average, with proprietary models at 23.7% median violation versus 72.8% for open-weight models, indicating they are not yet safe for clinical use.