MemGym: a Long-Horizon Memory Environment for LLM Agents

• URL: http://arxiv.org/abs/2605.20833v1
• Date: Wed, 20 May 2026 07:25:33 GMT
• Title: MemGym: a Long-Horizon Memory Environment for LLM Agents
• Abstract summary: We present MemGym, a benchmark for agentic memory that unifies existing agent gyms and in-house memory-grounded pipelines.<n>MemGym reports memory-isolated scores that decouple memory performance from reasoning, retrieval, and tool-use ability.<n>Our synthetic pipelines for MEMGYM-CODEQA and MEMGYM-DR are length-controllable, ablation-verified at every stage, and tightly aligned with downstream scenarios.
• Score: 69.79226770543049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Memory is a central capability for LLM agents operating across long-horizon tasks. Existing memory benchmarks predominantly evaluate retention of personalized information in multi-turn chat scenarios, overlooking the dynamic memory formation that occurs during extended agent execution. Consequently, the memory systems they produce transfer poorly to realistic agentic environments, such as coding and web navigation. We present MemGym, a benchmark for agentic memory that unifies existing agent gyms and in-house memory-grounded pipelines behind one memory-reasoning interface. MemGym spans five evaluation tracks grouped into four agentic regimes: tool-use dialogue (tau2-bench), multi-turn deep-research search (MEMGYM-DR), coding (SWE-Gym and MEMGYM-CODEQA), and computer use (WebArena-Infinity). MemGym reports memory-isolated scores that decouple memory performance from reasoning, retrieval, and tool-use ability, so memory strategies can be ranked without those confounders. Our synthetic pipelines for MEMGYM-CODEQA and MEMGYM-DR are length-controllable, ablation-verified at every stage, and tightly aligned with downstream scenarios. To make evaluation on coding environments academically tractable, we train MemRM, a lightweight reward model (Qwen3-1.7B fine-tuned with QLoRA) that scores compression quality as a fast scalar read in place of full Docker rollouts.

Related papers

• Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents [72.3752981234916]
  Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.<n>We present Memory as a Controlled Process (MemCon), a framework that models memory operations as a Markov Decision Process.<n>MemCon consistently outperforms multiple memory baselines by up to 15.2 points in task success while reducing token consumption by 5-20%.
  arXiv  Detail & Related papers  (2026-07-15T08:32:40Z)
• Beyond Similarity: Trustworthy Memory Search for Personal AI Agents [25.265839311088516]
  We study memory search as a trust boundary in personal AI agents.<n>MemGate is a lightweight and deployable memory plug-in for trustworthy memory search.
  arXiv  Detail & Related papers  (2026-06-04T11:54:29Z)
• eMEM: A Hybrid Spatio-Temporal Memory System For Embodied Agents [1.8047694351309203]
  We present eMEM, a hybrid graph-based memory system for embodied agents operating in physical environments.<n>eMEM fills this gap with a multi-index architecture (ITE for structured storage, hlib for approximate nearest neighbour semantic search, and an R-tree for spatial queries)<n>We also introduce eMEM-Bench v1, a benchmark we construct over ProcTHOR-10K scenes for embodied memory evaluation.
  arXiv  Detail & Related papers  (2026-06-02T09:22:12Z)
• LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleagues [80.29362825271768]
  LongMemEval-V2 is a benchmark for evaluating whether memory systems can help agents acquire the experience needed to become knowledgeable colleagues in customized environments.<n>LME-V2 contains 451 manually curated questions covering five core memory abilities for web agents.<n>We propose a suite of two memory methods: AgentRunbook-R, an efficient RAG-based memory with knowledge pools for raw state observations, events, and strategy notes, and AgentRunbook-C, which stores trajectories as files and invokes a coding agent to gather evidence in an augmented sandbox.
  arXiv  Detail & Related papers  (2026-05-12T17:59:34Z)
• SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory [40.68349164818526]
  We introduce SAGE, a Self-evolving Agentic Graph-memory Engine that models graph memory as a dynamic long-term memory substrate.<n> SAGE couples two roles: a memory writer that incrementally constucts structured graph memory from interaction histories, and a Graph Foundation Model-based memory reader to perform retrieval and provide feedback to the memory writer.
  arXiv  Detail & Related papers  (2026-05-12T12:47:43Z)
• Lightweight LLM Agent Memory with Small Language Models [27.343533336242913]
  LightMem is a lightweight memory system for better agent memory driven by Small Language Models (SLMs)<n>We organize memory into short-term memory (STM) for immediate conversational context, mid-term memory (MTM) for reusable interaction summaries, and long-term memory (LTM) for consolidated knowledge.<n>Experiments show gains across model scales, with an average F1 improvement of about 2.5 on LoCoMo, more effective and low median latency (83 ms retrieval; 581 ms end-to-end)
  arXiv  Detail & Related papers  (2026-04-09T04:51:07Z)
• MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution [52.29303869050117]
  Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction.<n>MemMA is a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths.
  arXiv  Detail & Related papers  (2026-03-19T10:15:59Z)
• MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks [55.145729491377374]
  Existing evaluations of agents with memory typically assess memorization and action in isolation.<n>We introduce MemoryArena, a unified evaluation gym for benchmarking agent memory in multi-session Memory-Agent-Environment loops.<n> MemoryArena supports evaluation across web navigation, preference-constrained planning, progressive information search, and sequential formal reasoning.
  arXiv  Detail & Related papers  (2026-02-18T09:49:14Z)
• 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)
• Memory OS of AI Agent [3.8665965906369375]
  Large Language Models (LLMs) face a crucial challenge from fixed context windows and inadequate memory management.<n>We propose a Memory Operating System, i.e., MemoryOS, to achieve comprehensive and efficient memory management for AI agents.
  arXiv  Detail & Related papers  (2025-05-30T15:36:51Z)

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.