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BioLLMAgent bridges the gap between interpretable but unrealistic RL models and realistic but opaque LLM agents, offering a "computational sandbox" for testing psychiatric hypotheses.
LLM-powered agents can autonomously orchestrate 6G network slices from natural language intents, but only with a carefully designed architecture and prompts that encode domain-specific constraints.
