# General Agentic Memory Via Deep Research

arXiv: https://arxiv.org/abs/2511.18423 · HTML: https://mlsanity.com/paper/2511.18423/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 19 references that are in the map.
- First posted on arXiv 23 Nov 2025.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 17 of 17)

- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [RULER: What's the Real Context Size of Your Long-Context Language Models?](https://mlsanity.com/paper/2404.06654/) (2024)
- [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [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)
- [Extending Context Window of Large Language Models via Positional Interpolation](https://mlsanity.com/paper/2306.15595/) (2023)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [The NarrativeQA Reading Comprehension Challenge](https://mlsanity.com/paper/1712.07040/) (2017)
- [Agent Laboratory: Using LLM Agents as Research Assistants](https://mlsanity.com/paper/2501.04227/) (2025)
- [MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent](https://mlsanity.com/paper/2507.02259/) (2025)
- [LightMem: Lightweight and Efficient Memory-Augmented Generation](https://mlsanity.com/paper/2510.18866/) (2025)
- [Memory OS of AI Agent](https://mlsanity.com/paper/2506.06326/) (2025)
- [MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution](https://mlsanity.com/paper/2403.17927/) (2024)
- [Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects](https://mlsanity.com/paper/2401.03428/) (2024)

## Built on by (most cited first, 30 of 47)

- [Memory in the Age of AI Agents](https://mlsanity.com/paper/2512.13564/) (2025)
- [SimpleMem: Efficient Lifelong Memory for LLM Agents](https://mlsanity.com/paper/2601.02553/) (2026)
- [Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering](https://mlsanity.com/paper/2604.08224/) (2026)
- [A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents](https://mlsanity.com/paper/2602.06052/) (2026)
- [Graph-based Agent Memory: Taxonomy, Techniques, and Applications](https://mlsanity.com/paper/2602.05665/) (2026)
- [Mem-T: Densifying Rewards for Long-Horizon Memory Agents](https://mlsanity.com/paper/2601.23014/) (2026)
- [Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning](https://mlsanity.com/paper/2601.04726/) (2026)
- [AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation](https://mlsanity.com/paper/2601.08323/) (2026)
- [MemoBrain: Executive Memory as an Agentic Brain for Reasoning](https://mlsanity.com/paper/2601.08079/) (2026)
- [Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory](https://mlsanity.com/paper/2602.06025/) (2026)
- [AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents](https://mlsanity.com/paper/2512.23343/) (2025)
- [PM-Bench: Evaluating Prospective Memory in LLM Agents](https://mlsanity.com/paper/2607.12385/) (2026)
- [HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents](https://mlsanity.com/paper/2604.16839/) (2026)
- [D-Mem: A Dual-Process Memory System for LLM Agents](https://mlsanity.com/paper/2603.18631/) (2026)
- [AttriMem: Attribution-Guided Process Feedback for Agent Memory Construction](https://mlsanity.com/paper/2607.21106/) (2026)
- [HyMem: Hybrid Memory Architecture with Dynamic Retrieval Scheduling](https://mlsanity.com/paper/2602.13933/) (2026)
- [OpenSage: Self-programming Agent Generation Engine](https://mlsanity.com/paper/2602.16891/) (2026)
- [Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents](https://mlsanity.com/paper/2605.25971/) (2026)
- [Bi-Mem: Bidirectional Construction of Hierarchical Memory for Personalized LLMs via Inductive-Reflective Agents](https://mlsanity.com/paper/2601.06490/) (2026)
- [MemPro: Agentic Memory Systems as Evolvable Programs](https://mlsanity.com/paper/2606.00619/) (2026)
- [Zero-Mem: Zero-Token Memory Operations for LLM Agents](https://mlsanity.com/paper/2607.29377/) (2026)
- [ShardMemo: Scope-Before-Routing for Agentic Memory Retrieval](https://mlsanity.com/paper/2601.21545/) (2026)
- [CLAG: Adaptive Memory Organization via Agent-Driven Clustering for Small Language Model Agents](https://mlsanity.com/paper/2603.15421/) (2026)
- [Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning](https://mlsanity.com/paper/2606.24428/) (2026)
- [Stop When Memory Suffices: Evidence-Conditioned Progressive Execution for LLM Agents](https://mlsanity.com/paper/2608.01285/) (2026)
- [Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents](https://mlsanity.com/paper/2609.16053/) (2026)
- [Revolutionizing Long-Term Memory in AI: New Horizons with High-Capacity and High-Speed Storage](https://mlsanity.com/paper/2602.16192/) (2026)
- [Joint Optimization of Multi-agent Memory System](https://mlsanity.com/paper/2603.12631/) (2026)
- [SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval](https://mlsanity.com/paper/2604.14712/) (2026)
- [ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems](https://mlsanity.com/paper/2604.23878/) (2026)

## Abstract (arXiv)

Memory is critical for AI agents, yet the widely-adopted static memory, aiming to create readily available memory in advance, is inevitably subject to severe information loss. To address this limitation, we propose a novel framework called general agentic memory (GAM). GAM follows the principle of "just-in time (JIT) compilation" where it focuses on creating optimized contexts for its client at runtime while keeping only simple but useful memory during the offline stage. To this end, GAM employs a duo-design with the following components. 1) Memorizer, which highlights key historical information using a lightweight memory, while maintaining complete historical information within a universal page-store. 2) Researcher, which retrieves and integrates useful information from the page-store for its online request guided by the pre-constructed memory. This design allows GAM to effectively leverage the agentic capabilities and test-time scalability of frontier large language models (LLMs), while also facilitating end-to-end performance optimization through reinforcement learning. In our experimental study, we demonstrate that GAM achieves substantial improvement on various memory-grounded task completion scenarios against existing memory systems.