# Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity

arXiv: https://arxiv.org/abs/2602.03315 · HTML: https://mlsanity.com/paper/2602.03315/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 15 of 15)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [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)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [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)
- [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://mlsanity.com/paper/2405.14831/) (2024)
- [Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning](https://mlsanity.com/paper/2508.19828/) (2025)
- [MIRIX: Multi-Agent Memory System for LLM-Based Agents](https://mlsanity.com/paper/2507.07957/) (2025)

## Built on by (most cited first, 11 of 11)

- [SmartSearch: How Ranking Beats Structure for Conversational Memory Retrieval](https://mlsanity.com/paper/2603.15599/) (2026)
- [ByteRover: Agent-Native Memory Through LLM-Curated Hierarchical Context](https://mlsanity.com/paper/2604.01599/) (2026)
- [Mi-Memory: A Lifecycle Memory Framework for Personal AI](https://mlsanity.com/paper/2607.18975/) (2026)
- [When Users Don't Ask: Benchmarking Context-Driven Memory Retrieval in Conversational Agents](https://mlsanity.com/paper/2609.03467/) (2026)
- [Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles](https://mlsanity.com/paper/2605.22177/) (2026)
- [Learning on the Job: Continual Learning from Deployment Feedback for Frozen-Weights Agents](https://mlsanity.com/paper/2607.22157/) (2026)
- [When Does Selection Replace Extraction? A Pre-Registered Test of Agent Memory with a Typed Decision Model](https://mlsanity.com/paper/2609.34227/) (2026)
- [PSD: Pseudo Self-Distillation of Memory Representation Capabilities for LLM Agents](https://mlsanity.com/paper/2609.23449/) (2026)
- [Realize What Matters: Principled Context Representation for Large-Scale Reasoning](https://mlsanity.com/paper/2609.27173/) (2026)
- [EnSIMem: Entity-Structured Indexing for Long-Term Agent Memory](https://mlsanity.com/paper/2609.27279/) (2026)
- [MemCo: Memory-Centric Collaboration for Generalizing LLM Agents to Unseen Environments](https://mlsanity.com/paper/2610.07376/) (2026)

## Abstract (arXiv)

Agent memory systems must accommodate continuously growing information while supporting efficient, context-aware retrieval for downstream tasks. Abstraction is essential for scaling agent memory, yet it often comes at the cost of specificity, obscuring the fine-grained details required for effective reasoning. We introduce Memora, a harmonic memory representation that structurally balances abstraction and specificity. Memora organizes information via its primary abstractions that index concrete memory values and consolidate related updates into unified memory entries, while cue anchors expand retrieval access across diverse aspects of the memory and connect related memories. Building on this structure, we employ a retrieval policy that actively exploits these memory connections to retrieve relevant information beyond direct semantic similarity. Theoretically, we show that standard Retrieval-Augmented Generation (RAG) and Knowledge Graph (KG)-based memory systems emerge as special cases of our framework. Empirically, Memora establishes a new state-of-the-art on the LoCoMo and LongMemEval benchmarks, demonstrating better retrieval relevance and reasoning effectiveness as memory scales.