HORMA builds a hierarchical memory structure from agent experiences and trains a lightweight RL navigator to retrieve minimal sufficient context, yielding better task performance with at most 22.17% of baseline token usage on ALFWorld, LoCoMo, and LongMemEval.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Agentic memory is lookup-based retrieval, not weight-based consolidation, creating a generalization ceiling on novel tasks and structural vulnerability to memory poisoning.
Behavior latticing synthesizes connections across unstructured user interactions to generate insights into underlying motivations, yielding deeper and more accurate user understanding than task-only models.
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Behavior Latticing: Inferring User Motivations from Unstructured Interactions
Behavior latticing synthesizes connections across unstructured user interactions to generate insights into underlying motivations, yielding deeper and more accurate user understanding than task-only models.