How We Measured the Safety of our Mental Health AI - Spring Health
<a href="https://news.google.com/rss/articles/CBMihwFBVV95cUxNZlZfbFNBZlZWb1pxbmVpMG1oR2Z6THRiby1WakxzRHJXMGc4WVJPQTR4VnhUMFpsQ242Mmt2UHY5S0txd0NqU1E4bUJTcWt0WnJ2dWllbXhQa0pWUXhIVk1FY3VzYUVqNm14MGlOTC01M0kwcndGdXlKc19EV1dTRVlKXzRpSUU?oc=5" target="_blank">How We Measured the Safety of our Mental Health AI</a> <font color="#6f6f6f">Spring Health</font>
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Causal Scene Narration with Runtime Safety Supervision for Vision-Language-Action Driving
arXiv:2604.01723v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models for autonomous driving must integrate diverse textual inputs, including navigation commands, hazard warnings, and traffic state descriptions, yet current systems often present these as disconnected fragments, forcing the model to discover on its own which environmental constraints are relevant to the current maneuver. We introduce Causal Scene Narration (CSN), which restructures VLA text inputs through intent-constraint alignment, quantitative grounding, and structured separation, at inference time with zero GPU cost. We complement CSN with Simplex-based runtime safety supervision and training-time alignment via Plackett-Luce DPO with negative log-likelihood (NLL) regularization. A multi-town closed-loop CA
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