# EvoMem: Improving Multi-Agent Planning with Dual-Evolving Memory arXiv: https://arxiv.org/abs/2511.01912 ยท HTML: https://mlsanity.com/paper/2511.01912/ ## Key facts - Cited by 6 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 32 of its 38 references that are in the map. - First posted on arXiv 1 Nov 2025. ## Placed in - LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/) ## Builds on (most cited first, 30 of 32) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022) - [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024) - [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025) - [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025) - [MemoryBank: Enhancing Large Language Models with Long-Term Memory](https://mlsanity.com/paper/2305.10250/) (2023) - [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024) - [Agent Workflow Memory](https://mlsanity.com/paper/2409.07429/) (2024) - [PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://mlsanity.com/paper/2206.10498/) (2022) - [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (2023) - [The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity](https://mlsanity.com/paper/2506.06941/) (2025) - [PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization](https://mlsanity.com/paper/2310.16427/) (2023) - [OS-Copilot: Towards Generalist Computer Agents with Self-Improvement](https://mlsanity.com/paper/2402.07456/) (2024) - [LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks](https://mlsanity.com/paper/2402.01817/) (2024) - [NATURAL PLAN: Benchmarking LLMs on Natural Language Planning](https://mlsanity.com/paper/2406.04520/) (2024) - [SCM: Enhancing Large Language Model with Self-Controlled Memory Framework](https://mlsanity.com/paper/2304.13343/) (2023) - [AIOS: LLM Agent Operating System](https://mlsanity.com/paper/2403.16971/) (2024) - [Planning In Natural Language Improves LLM Search For Code Generation](https://mlsanity.com/paper/2409.03733/) (2024) - [Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory](https://mlsanity.com/paper/2311.08719/) (2023) - [KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents](https://mlsanity.com/paper/2403.03101/) (2024) - [Evolving Deeper LLM Thinking](https://mlsanity.com/paper/2501.09891/) (2025) - [SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling](https://mlsanity.com/paper/2501.19306/) (2025) - [Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing](https://mlsanity.com/paper/2402.00658/) (2024) - [Memory Augmented Large Language Models are Computationally Universal](https://mlsanity.com/paper/2301.04589/) (2023) - [Tool-Planner: Task Planning with Clusters across Multiple Tools](https://mlsanity.com/paper/2406.03807/) (2024) - [Exploring and Benchmarking the Planning Capabilities of Large Language Models](https://mlsanity.com/paper/2406.13094/) (2024) - [RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents](https://mlsanity.com/paper/2406.11132/) (2024) - [PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving](https://mlsanity.com/paper/2502.16111/) (2025) - [A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models](https://mlsanity.com/paper/2405.18208/) (2024) - [Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism](https://mlsanity.com/paper/2410.12859/) (2024) ## Built on by (most cited first, 6 of 6) - [A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents](https://mlsanity.com/paper/2602.06052/) (2026) - [Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations](https://mlsanity.com/paper/2602.19320/) (2026) - [TodyComm: Task-Oriented Dynamic Communication for Multi-Round LLM-based Multi-Agent System](https://mlsanity.com/paper/2602.03688/) (2026) - [EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales](https://mlsanity.com/paper/2605.11136/) (2026) - [Embodied Task Planning via Graph-Informed Action Generation with Large Language Models](https://mlsanity.com/paper/2601.21841/) (2026) - [EvoDiagram: Agentic Editable Diagram Creation via Design Expertise Evolution](https://mlsanity.com/paper/2604.09568/) (2026) ## Abstract (arXiv) Planning has been a cornerstone of artificial intelligence for solving complex problems, and recent progress in LLM-based multi-agent frameworks have begun to extend this capability. However, the role of human-like memory within these frameworks remains largely unexplored. Understanding how agents coordinate through memory is critical for natural language planning, where iterative reasoning, constraint tracking, and error correction drive the success. Inspired by working memory model in cognitive psychology, we present EvoMem, a multi-agent framework built on a dual-evolving memory mechanism. The framework consists of three agents (Constraint Extractor, Verifier, and Actor) and two memory modules: Constraint Memory (CMem), which evolves across queries by storing task-specific rules and constraints while remains fixed within a query, and Query-feedback Memory (QMem), which evolves within a query by accumulating feedback across iterations for solution refinement. Both memory modules are reset at the end of each query session. Evaluations on trip planning, meeting planning, and calendar scheduling show consistent performance improvements, highlighting the effectiveness of EvoMem. This success underscores the importance of memory in enhancing multi-agent planning.