Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems

• URL: http://arxiv.org/abs/2604.03295v1
• Date: Fri, 27 Mar 2026 19:34:23 GMT
• Title: Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems
• Abstract summary: Large language model (LLM) multi-agent systems can scale along two distinct dimensions.<n>We introduce a conceptual scaling view of multi-agent systems that jointly considers team size and lifelong learning ability.<n>We propose textbfLLMA-Mem, a lifelong memory framework for LLM multi-agent systems under flexible memory topologies.
• Score: 36.7638915908205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM) multi-agent systems can scale along two distinct dimensions: by increasing the number of agents and by improving through accumulated experience over time. Although prior work has studied these dimensions separately, their interaction under realistic cost constraints remains unclear. In this paper, we introduce a conceptual scaling view of multi-agent systems that jointly considers team size and lifelong learning ability, and we study how memory design shares this landscape. To this end, we propose \textbf{LLMA-Mem}, a lifelong memory framework for LLM multi-agent systems under flexible memory topologies. We evaluate LLMA-Mem on \textsc{MultiAgentBench} across coding, research, and database environments. Empirically, LLMA-Mem consistently improves long-horizon performance over baselines while reducing cost. Our analysis further reveals a non-monotonic scaling landscape: larger teams do not always produce better long-term performance, and smaller teams can outperform larger ones when memory better supports the reuse of experience. These findings position memory design as a practical path for scaling multi-agent systems more effectively and more efficiently over time.

Related papers

• Rosetta Memory: Adaptive Memory for Cross-LLM Agents [16.39618390394249]
  Memory is the key component for transforming a stateless LLM into a persistent, evolving agent through experience accumulation, long-horizon planning, and continual self-improvement.<n>In practice, users frequently switch between LLMs, for example using Claude for coding and GPT for writing across tasks, or routing different steps to different backbones within a single task for cost-effective trade-offs.<n>Making upstream memory effectively adapt to and activate downstream LLMs remains a critical yet underexplored problem.<n>Specifically, we approach the above upstream-downstream memory adaptation problem from both the write and read sides, and design two profile-conditioned
  arXiv  Detail & Related papers  (2026-06-05T13:50:59Z)
• Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents [27.2861945963127]
  Memory-augmented LLM agents enable interactions that extend beyond finite context windows.<n>Training such agents with reinforcement learning in multi-session environments is challenging.<n>We introduce Memory-R2, a training framework for memory-augmented LLM agents.
  arXiv  Detail & Related papers  (2026-05-20T22:02:00Z)
• RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies [54.23445842621374]
  Memory is critical for long-horizon and history-dependent robotic manipulation.<n>Recent vision-language-action (VLA) models have begun to incorporate memory mechanisms.<n>We introduce RoboMME: a large-scale standardized benchmark for evaluating and advancing VLA models.
  arXiv  Detail & Related papers  (2026-03-04T21:59:32Z)
• LatentMem: Customizing Latent Memory for Multi-Agent Systems [44.59989123744384]
  We propose LatentMem, a learnable multi-agent memory framework designed to customize agent-specific memories in a token-efficient manner.<n>Specifically, LatentMem comprises an experience bank that stores raw interaction trajectories in a lightweight form, and a memory composer that synthesizes compact latent memories conditioned on retrieved experience and agent-specific contexts.
  arXiv  Detail & Related papers  (2026-02-03T03:03:16Z)
• EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory [63.84216832544323]
  EvolMem is a new benchmark for assessing multi-session memory capabilities of large language models (LLMs) and agent systems.<n>To construct the benchmark, we introduce a hybrid data synthesis framework that consists of topic-initiated generation and narrative-inspired transformations.<n>Extensive evaluation reveals that no LLM consistently outperforms others across all memory dimensions.
  arXiv  Detail & Related papers  (2026-01-07T03:14:42Z)
• Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents [76.76004970226485]
  Long-term memory is a critical capability for multimodal large language model (MLLM) agents.<n>Mem-Gallery is a new benchmark for evaluating multimodal long-term conversational memory in MLLM agents.
  arXiv  Detail & Related papers  (2026-01-07T02:03:13Z)
• Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents [57.38404718635204]
  Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows.<n>Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components.<n>We propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.
  arXiv  Detail & Related papers  (2026-01-05T08:24:16Z)
• Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning [89.55738101744657]
  Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless.<n>We present Memory-R1, a reinforcement learning framework that equips LLMs with the ability to actively manage and utilize external memory.
  arXiv  Detail & Related papers  (2025-08-27T12:26:55Z)
• Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents [19.04968632268433]
  We propose a hierarchical memory architecture for Large Language Model Agents (LLM Agents)<n>Each memory vector is embedded with a positional index encoding pointing to its semantically related sub-memories in the next layer.<n>During the reasoning phase, an index-based routing mechanism enables efficient, layer-by-layer retrieval without performing exhaustive similarity computations.
  arXiv  Detail & Related papers  (2025-07-23T12:45:44Z)
• G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems [44.844636264484905]
  Large language model (LLM)-powered multi-agent systems (MAS) have demonstrated cognitive and execution capabilities that far exceed those of single LLM agents.<n>We introduce G-Memory, a hierarchical, agentic memory system for MAS inspired by organizational memory theory.<n>G-Memory improves success rates in embodied action and accuracy in knowledge QA by up to $20.89%$ and $10.12%$, respectively.
  arXiv  Detail & Related papers  (2025-06-09T03:43:46Z)
• A Survey on the Memory Mechanism of Large Language Model based Agents [66.4963345269611]
  Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems. The key component to support agent-environment interactions is the memory of the agents.
  arXiv  Detail & Related papers  (2024-04-21T01:49:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.