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

2026-10-08

AI Summary

This study addresses the challenge faced by LLM-based agents in transforming continuous experiences into reusable knowledge to support long-term autonomous operation. To this end, it proposes Hippocam, a hierarchical memory architecture that emulates the selective retention and progressive consolidation mechanisms of human memory through nested intention modeling and recursive prefix integration. Furthermore, this work introduces a pioneering "use-it-or-lose-it" dynamic memory model that effectively bridges working contexts, long-term memory, and skill acquisition without requiring parameter updates. The primary contribution lies in enabling lifelong learning via hierarchical detail restoration, thereby empowering agents to continuously evolve their capabilities from accumulated experience and achieve sustained knowledge consolidation over time.

Abstract

The evolution of Large Language Model agents from single-task execution to long-term autonomous operation highlights the critical challenge of transforming continuous experiences into reusable knowledge. To address this, we propose Hippocam, a hierarchical memory and continual learning architecture. Hippocam draws inspiration from two characteristics of human memory: cognitive processes selectively maintain information relevant to current goals, while long-term memories form gradually through repeated consolidation. Accordingly, Hippocam structures an agent's ongoing work as nested intents. The active context remains centered on the current intent, while completed intents are consolidated into the task-relevant outcomes and state needed for subsequent work, rather than carrying forward their full working details. Concurrently, a recursive prefix consolidation mechanism repeatedly consolidates earlier history, causing long-unused experiences to become increasingly abstract. Original interactions are preserved, allowing the agent to progressively recover finer-grained details through the hierarchy and stop once sufficient information is available. Crucially, when past experiences are recalled and reintegrated into active work, they undergo subsequent consolidation alongside new experiences, thereby being reinforced, supplemented, and updated. Through this memory dynamic of use and disuse, Hippocam connects working context, long-term memory, knowledge accumulation, and skill learning within a single continuously evolving experiential process. This enables agents to learn and evolve capabilities through their own experiences without parameter updates.

Problem

• Large Language Model agents
• long-term autonomous operation
• experience consolidation
• reusable knowledge
• continual learning

Innovation

• Hierarchical Memory
• Continual Learning
• Intent-Structured Consolidation
• Recursive Prefix Consolidation
• Parameter-Free Learning