REMI combines a personal causal graph, counterfactual traversal, and schema-based planning with an LLM to generate explainable, personalized lifestyle recommendations, claiming higher personalization and causal accuracy than baselines.
Generalization through memorization: Nearest neighbor language models,
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REMI: A Novel Causal Schema Memory Architecture for Personalized Lifestyle Recommendation Agents
REMI combines a personal causal graph, counterfactual traversal, and schema-based planning with an LLM to generate explainable, personalized lifestyle recommendations, claiming higher personalization and causal accuracy than baselines.