Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents
cs.CL, cs.AI
Submitted: 2026-09-13
Updated: 2026-09-13
Project page: https://langchain-ai.github.io/langmem
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- SMMBench: A Benchmark for Source-Distributed Multimodal Agent Memory
- Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
- Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers
- MemR$^3$: Memory Retrieval via Reflective Reasoning for LLM Agents
- From Local to Global: A Graph RAG Approach to Query-Focused Summarization
- LightMem: Lightweight and Efficient Memory-Augmented Generation
- FSFM: A Biologically-Inspired Framework for Selective Forgetting of Agent Memory
- Memory in the Age of AI Agents
- MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents
- SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation
- MemOS: A Memory OS for AI System
- What Deserves Memory: Adaptive Memory Distillation for LLM Agents
- MemGPT: Towards LLMs as Operating Systems
- Zep: A Temporal Knowledge Graph Architecture for Agent Memory
- MIRIX: Multi-Agent Memory System for LLM-Based Agents
- MEMORYLLM: Towards Self-Updatable Large Language Models
- M+: Extending MemoryLLM with Scalable Long-Term Memory
- LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
- General Agentic Memory Via Deep Research
- PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
Related papers
- Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
- Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
- Subliminal Steering: Stronger Encoding of Hidden Signals
- MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
- The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
- Untangling the Mechanisms of Misleading Context in Medical Question Answering