# MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents

arXiv: https://arxiv.org/abs/2604.04853 · HTML: https://mlsanity.com/paper/2604.04853/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 6 Apr 2026.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)
- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 14 of 14)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025)
- [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024)
- [Zep: A Temporal Knowledge Graph Architecture for Agent Memory](https://mlsanity.com/paper/2501.13956/) (2025)
- [Memory in the Age of AI Agents](https://mlsanity.com/paper/2512.13564/) (2025)
- [MemOS: A Memory OS for AI System](https://mlsanity.com/paper/2507.03724/) (2025)
- [Agent Lightning: Train ANY AI Agents with Reinforcement Learning](https://mlsanity.com/paper/2508.03680/) (2025)
- [MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models](https://mlsanity.com/paper/2505.22101/) (2025)
- [Towards LifeSpan Cognitive Systems](https://mlsanity.com/paper/2409.13265/) (2024)

## Built on by (most cited first, 16 of 16)

- [Securing LLM-Agent Long-Term Memory Against Poisoning: Non-Malleable, Origin-Bound Authority with Machine-Checked Guarantees](https://mlsanity.com/paper/2606.24322/) (2026)
- [MemMark: State-Evolution Attribution Watermarking for Agent Long-Term Memory Systems](https://mlsanity.com/paper/2605.25002/) (2026)
- [User as Code: Executable Memory for Personalized Agents](https://mlsanity.com/paper/2606.16707/) (2026)
- [From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI](https://mlsanity.com/paper/2606.14502/) (2026)
- [PROJECTMEM: A Local-First, Event-Sourced Memory and Judgment Layer for AI Coding Agents](https://mlsanity.com/paper/2606.12329/) (2026)
- [User as Engram: Internalizing Per-User Memory as Local Parametric Edits](https://mlsanity.com/paper/2606.19172/) (2026)
- [Mi-Memory: A Lifecycle Memory Framework for Personal AI](https://mlsanity.com/paper/2607.18975/) (2026)
- [Mnemon: Raw Records, Fast Judgments, Slow Thoughts](https://mlsanity.com/paper/2609.36059/) (2026)
- [Memory as Metabolism: A Design for Companion Knowledge Systems](https://mlsanity.com/paper/2604.12034/) (2026)
- [Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents](https://mlsanity.com/paper/2608.15008/) (2026)
- [Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo](https://mlsanity.com/paper/2604.11563/) (2026)
- [Beyond Memory Leaderboards: Evaluating Scientific Memory as Budgeted Context Restoration](https://mlsanity.com/paper/2607.16848/) (2026)
- [Setoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data](https://mlsanity.com/paper/2607.27056/) (2026)
- [Blast Radius](https://mlsanity.com/paper/2608.07440/) (2026)
- [The Sleeping Agent: What Gist-Based Context Compression Loses and Why](https://mlsanity.com/paper/2608.11775/) (2026)
- [D2ACCI: A Dual-Loop Diagnostic Protocol for Evidence-Preserving Agent Memory](https://mlsanity.com/paper/2608.17756/) (2026)

## Abstract (arXiv)

Large Language Model (LLM) agents require persistent memory to maintain personalization, factual continuity, and long-horizon reasoning, yet standard context-window and retrieval-augmented generation (RAG) pipelines degrade over multi-session interactions. We present MemMachine, an open-source memory system that integrates short-term, long-term episodic, and profile memory within a ground-truth-preserving architecture that stores entire conversational episodes and reduces lossy LLM-based extraction. MemMachine uses contextualized retrieval that expands nucleus matches with surrounding context, improving recall when relevant evidence spans multiple dialogue turns. Across benchmarks, MemMachine achieves strong accuracy-efficiency tradeoffs: on LoCoMo it reaches 0.9169 using gpt4.1-mini; on LongMemEvalS (ICLR 2025), a six-dimension ablation yields 93.0 percent accuracy, with retrieval-stage optimizations -- retrieval depth tuning (+4.2 percent), context formatting (+2.0 percent), search prompt design (+1.8 percent), and query bias correction (+1.4 percent) -- outperforming ingestion-stage gains such as sentence chunking (+0.8 percent). GPT-5-mini exceeds GPT-5 by 2.6 percent when paired with optimized prompts, making it the most cost-efficient setup. Compared to Mem0, MemMachine uses roughly 80 percent fewer input tokens under matched conditions. A companion Retrieval Agent adaptively routes queries among direct retrieval, parallel decomposition, or iterative chain-of-query strategies, achieving 93.2 percent on HotpotQA-hard and 92.6 percent on WikiMultiHop under randomized-noise conditions. These results show that preserving episodic ground truth while layering adaptive retrieval yields robust, efficient long-term memory for personalized LLM agents.