Title: G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-agent systems

agent memory

LLM agent
Abstract: Large language model (LLM)-powered multi-agent systems (MAS) have demonstrated cognitive and execution capabilities that far exceed those of single LLM agents, yet their capacity for self-evolution remains hampered by underdeveloped memory architectures. Upon close inspection, we are alarmed to discover that prevailing MAS memory mechanisms (1) are overly simplistic, completely disregarding the nuanced inter-agent collaboration trajectories, and (2) lack cross-trial and agent-specific customization, in stark contrast to the expressive memory developed for single agents. To bridge this gap, we introduce G-Memory, a hierarchical, agentic memory system for MAS inspired by organizational memory theory, which manages the lengthy MAS interaction via a three-tier graph hierarchy: insight, query, and interaction graphs. Upon receiving a new user query, G-Memory performs bi-directional memory traversal to retrieve both \textit{high-level, generalizable insights} that enable the system to leverage cross-trial knowledge, and \textit{fine-grained, condensed interaction trajectories} that compactly encode prior collaboration experiences. Upon task execution, the entire hierarchy evolves by assimilating new collaborative trajectories, nurturing the progressive evolution of agent teams. Extensive experiments across five benchmarks, three LLM backbones, and three popular MAS frameworks demonstrate that G-Memory improves success rates in embodied action and accuracy in knowledge QA by up to $20.89\\%$ and $10.12\\%$, respectively, without any modifications to the original frameworks.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces G-Memory, a novel hierarchical and agentic memory architecture for Large Language Model (LLM)-driven Multi-Agent Systems (MAS). It aims to fill the gap of sophisticated memory mechanisms in MAS, which are currently limited compared to single-agent systems. G-Memory proposes a three-tier memory hierarchy: Insight Graph, Encodes high-level, generalizable lessons from past collaborative experiences. Query Graph, Stores task-specific meta information and tracks relationships between queries. Interaction Graph, Contains fine-grained, role-specific utterances and interactions for each query/task.Upon a new query, G-Memory performs efficient bi-directional retrieval, providing each agent with high-level insights and relevant historical trajectories tailored to their role. The system updates all memory levels after each trial, supporting self-evolution and improved performance. Empirical results show significant performance gains across various benchmarks, frameworks, and LLMs, without excessive resource overhead. The framework is modular and plug-in, compatible with different LLM-based MAS designs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \nThe authors clearly identify the shortcomings of existing MAS memory systems, contrasting them with the requirements of MAS. The hierarchical design is theoretically motivated, practically justified, and systematically implemented. The cross-agent, multi-level approach with role- and trial-adaptive retrieval is novel.\n\nWeaknesses: The paper admits that tasks are limited to five benchmarks, mainly QA, embodied action, and games. Generality to other domains is unproven. Some lower-level details, such as precise prompt engineering and insight validation, rely on in-LLM processing. The robustness of these steps is not deeply analyzed.\n\nQUESTIONS:\n- How does the memory graph scale with increasing agents/queries/tasks?\n   - Are there mechanisms for automatic pruning or compression of obsolete information?\n\nLIMITATIONS:\nThe paper discusses generalizability and suggests future evaluation on more diverse domains.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces G-Memory, a hierarchical memory system designed to enhance the self-evolution capabilities of multi-agent systems (MAS) by addressing the limitations of existing memory architectures. G-Memory organizes complex inter-agent interactions into a three-tier graph structure to enable efficient retrieval of high-level insights and fine-grained collaboration trajectories. The system improves task performance on different kinds of tasks. Experiments demonstrate its effectiveness as a plug-and-play module, facilitating adaptive and role-specific memory support for agents.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The writing is clear and engaging. Even though the memory system is complex, the authors explain it in a way that’s easy to follow. The paper flows well, the key points stand out, and the figures help a lot. It’s a solid, well-structured read.  \n\n2. The three-tier memory design is intuitive. It doesn’t just store past interactions but organizes them in a way that makes sense. The insight, query, and interaction graphs help agents pull out both high-level lessons and fine-grained details when needed.  \n\n3. It improves performance without blowing up costs. A 20%+ boost in some tasks while keeping token usage reasonable is impressive. A lot of memory systems add overhead, but this one seems efficient.  \n\n4. The experiments cover all the bases. They test performance, break down costs, run ablation studies, and include case studies. It’s thorough and convincing.  \n\n**Weaknesses**\n\n1. What about the cold-start problem? To my understanding, the system needs some initial memory to work well, but the paper doesn’t explain how that’s built or how sensitive performance is to the order of tasks. If early tasks feed bad data into memory, does that mess things up later? Some analysis here should be done.  \n\n2. How exactly are token costs calculated? Is it just completion tokens, or does it include prompts? And does the size of memory affect this? The memory pool becomes larger gradually, what's the impact on the cost? At the initial stage, the cost could be few, but the cost might be higher later, though the argtop operators could make the cost insensitive to the total volume of the memory pool. The details should be introduced.\n\n3. Why skip the 0-hop baseline in Figure 4? The hop expansion is supposed to fix noisy similarity matches, but without testing a no-hop version, it’s hard to tell if expanding actually helps or just adds more noise. Also, the motivation for hop expansion in line 173 \"the similarity may be only superficial or noisy\" seems weird to me. By using hop expansion, more nodes could be considered, and maybe more noise is introduced.\n\n4. The ranking/sparsification part feels handwavy. Eq. 7 mentions it, and the appendix only includes some prompts. How exactly does the LLM decide what to keep or drop? What's the effectivenss? A clearer explanation would make it easier to replicate.  \n\nOverall, it’s a good paper to me, but a few more details, especially on initialization and implementation, would make it even better. If authors could address my concerns, I could consider further raise my score.\n\nQUESTIONS:\nSee above weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces G-Memory, a hierarchical memory system for Large Language Model powered multi-agent systems (MAS). The authors identify that existing MAS memory architectures are oversimplified, lacking nuanced inter-agent collaboration tracking and cross-trial customization, hindering self-evolution. G-Memory addresses this via a three-tier graph hierarchy: Insight Graph, Query Graph and interaction graph. Upon a new query, G-Memory performs bidirectional traversal (upward for insights, downward for relevant interaction subgraphs) to provide role-specific memory cues. After task execution, the graphs evolve by assimilating new experiences. Evaluated across five benchmarks and three MAS frameworks, G-Memory improves success rates significantly. The system operates as a plug-and-play module, requiring no framework modifications.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Novel architecture: The three-tier graph hierarchy  innovatively captures multi-granularity memory needs, enabling both high-level reasoning  and fine-grained collaboration recall .\n2. Significant Performance Gains: G-Memory achieves state-of-the-art improvements across diverse tasks.\n3. Resource Efficiency: Despite its advanced capabilities, G-Memory incurs only marginal token overhead\n4. Seamless Integration: As a plug-and-play module, G-Memory integrates into mainstream MAS frameworks without structural modifications, demonstrating practical deployability.\n\nWeaknesses\n1. Cold-Start Graph Initialization: The paper lacks details on bootstrapping the three-tier graphs from scratch, which is critical for real-world deployment with no prior collaboration data.\n2. Ambiguous Graph Update Mechanisms: The update process for assimilating new experiences requires clarification:\n3. Safety and Robustness Gaps: Potential risks from retrieving misleading insights/interactions are underexplored, despite the impact statement noting amplification of incorrect reasoning.\n\nQUESTIONS:\n1. How are the three-tier graphs initialized during the bootstrapping process?\n2. Are all new queries incorporated into the memory? If not, what criteria filter queries for updates?\n3. How is the correctness of insights verified, and does insight quality impact G-Memory's performance?\n4. What safeguards prevent adverse effects from noisy/false insights or action trajectories (e.g., hallucinated lessons)?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}

comments: SUMMARY:
In this paper, the authors propose a three-layer graph memory, including insight graph, query graph, and interaction graph. The proposed G-memory enables agents to store, recall, and reason over past experiences more effectively.

STRENGTHS AND WEAKNESSES:
Strengths: 

1. The design of the three layers hierarchical memory framework is novel. It captures the high-level insights, intermediate queries, and interaction between agents. 

2. The authors conduct experiment across multiple domains, including knowledge reasoning, embodied action, game. Experimental results demonstrate the effectiveness of the proposed method.

3. The authors also conduct a series of ablation study to validate the performance.

Weakness:

1.  Some notations are not very clear. Such as:

      a. What is one epoch in MAS? In debate case, one round of agent speaking is one epoch; but in solely task solving case, what does one epoch mean?

      b. In equation (5), $\mathcal{N}^{+}$ is? Is it out-neighbor?

2. In the experiment section, it is expected to adopt more tasks to validate the effectiveness of the proposed method.

QUESTIONS:
question 1: see weakness 1

question 2: How about the performance of G-memory in more domains, such as on WebArena dataset or GAIA dataset? 

question 3: How about the latency of the G-memory; In the paper, the authors present the token cost, but the latency is not mentioned.

LIMITATIONS:
None
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-07-05