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

cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Comments: 33 pages, including references and appendices

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• MemGPT: Towards LLMs as Operating Systems
• From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs
• RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
• A-MEM: Agentic Memory for LLM Agents

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• Agents' Last Exam
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems