Use and Disuse: Intent-Structured Experience Consolidation for Memory and Learning in LLM Agents

The paper introduces Hippocam, a hierarchical memory and continual learning architecture for large language model agents that mirrors human memory processes. Hippocam organizes an agent’s work into nested intents, keeping the current intent active while consolidating completed intents into task‑relevant outcomes and states, and recursively abstracts earlier history to reduce detail over time. When recalled, past experiences are re‑integrated and further consolidated with new data, reinforcing and updating knowledge without requiring parameter updates.

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• llms
• agents