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SteinGate introduces a distributional safety certificate using Kernelized Stein Discrepancy to detect rare catastrophic tail events in safe reinforcement learning, dynamically adapting policy updates to reduce constraint violations while maintaining competitive returns.
Presents an LLM-driven framework for retrieving remote sensing data from cloud-based geospatial catalogues using natural language queries, with a focus on safety and adversarial robustness. The system integrates three agents for intent interpretation, API call generation, and risk management.