# Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning arXiv: https://arxiv.org/abs/2601.04726 ยท HTML: https://mlsanity.com/paper/2601.04726/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. - Builds on 17 of its 24 references that are in the map. - First posted on arXiv 8 Jan 2026. ## Placed in - LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/) - LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/) ## Builds on (most cited first, 17 of 17) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023) - [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) - [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) - [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) - [LightMem: Lightweight and Efficient Memory-Augmented Generation](https://mlsanity.com/paper/2510.18866/) (2025) - [From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs](https://mlsanity.com/paper/2504.15965/) (2025) - [MemGen: Weaving Generative Latent Memory for Self-Evolving Agents](https://mlsanity.com/paper/2509.24704/) (2025) - [MemEvolve: Meta-Evolution of Agent Memory Systems](https://mlsanity.com/paper/2512.18746/) (2025) - [From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs](https://mlsanity.com/paper/2410.14052/) (2024) - [Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents](https://mlsanity.com/paper/2507.22925/) (2025) - [General Agentic Memory Via Deep Research](https://mlsanity.com/paper/2511.18423/) (2025) - [SGMem: Sentence Graph Memory for Long-Term Conversational Agents](https://mlsanity.com/paper/2509.21212/) (2025) - [CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension](https://mlsanity.com/paper/2510.05520/) (2025) ## Built on by (most cited first, 17 of 17) - [Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations](https://mlsanity.com/paper/2602.19320/) (2026) - [Mem-T: Densifying Rewards for Long-Horizon Memory Agents](https://mlsanity.com/paper/2601.23014/) (2026) - [Coding Agents are Effective Long-Context Processors](https://mlsanity.com/paper/2603.20432/) (2026) - [Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents](https://mlsanity.com/paper/2606.06090/) (2026) - [SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent](https://mlsanity.com/paper/2605.24468/) (2026) - [Query-focused and Memory-aware Reranker for Long Context Processing](https://mlsanity.com/paper/2602.12192/) (2026) - [MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning](https://mlsanity.com/paper/2603.03379/) (2026) - [AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning](https://mlsanity.com/paper/2605.24486/) (2026) - [Zero-Mem: Zero-Token Memory Operations for LLM Agents](https://mlsanity.com/paper/2607.29377/) (2026) - [MemCoT: Test-Time Scaling through Memory-Driven Chain-of-Thought](https://mlsanity.com/paper/2604.08216/) (2026) - [R^2-Mem: Reflective Experience for Memory Search](https://mlsanity.com/paper/2605.13486/) (2026) - [AgentSpec: Understanding Embodied Agent Scaffolds Through Controlled Composition](https://mlsanity.com/paper/2606.14674/) (2026) - [Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents](https://mlsanity.com/paper/2608.15008/) (2026) - [CABLE: Extending the Reach of Memory Retrieval via Complementary Antecedent-Based Linking and Expansion](https://mlsanity.com/paper/2608.17911/) (2026) - [Event-Centric World Modeling with Memory-Augmented Retrieval for Embodied Decision-Making](https://mlsanity.com/paper/2604.07392/) (2026) - [MemFuse: Multi-Source Memory Fusion from Fragmented Observations](https://mlsanity.com/paper/2608.18704/) (2026) - [EdgeMem: LLM-Free Agent Memory Construction and Retrieval via Evidence-Preserving Multi-Anchor Hypergraph](https://mlsanity.com/paper/2609.05553/) (2026) ## Abstract (arXiv) Large language models (LLMs) are increasingly deployed as intelligent agents that reason, plan, and interact with their environments. To effectively scale to long-horizon scenarios, a key capability for such agents is a memory mechanism that can retain, organize, and retrieve past experiences to support downstream decision-making. However, most existing approaches organize and store memories in a flat manner and rely on simple similarity-based retrieval techniques. Even when structured memory is introduced, existing methods often struggle to explicitly capture the logical relationships among experiences or memory units. Moreover, memory access is largely detached from the constructed structure and still depends on shallow semantic retrieval, preventing agents from reasoning logically over long-horizon dependencies. In this work, we propose CompassMem, an event-centric memory framework inspired by Event Segmentation Theory. CompassMem organizes memory as an Event Graph by incrementally segmenting experiences into events and linking them through explicit logical relations. This graph serves as a logic map, enabling agents to perform structured and goal-directed navigation over memory beyond superficial retrieval, progressively gathering valuable memories to support long-horizon reasoning. Experiments on LoCoMo and NarrativeQA demonstrate that CompassMem consistently improves both retrieval and reasoning performance across multiple backbone models.