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AMemGym: Interactive Memory Benchmarking for Assistants in Long-Horizon Conversations

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
memoryagentlong-context
Abstract:

Long-horizon interactions between users and LLM-based assistants necessitates effective memory management, yet current approaches face challenges in training and evaluation of memory. Existing memory benchmarks rely on static, off-policy data as context, limiting evaluation reliability and scalability. To address these gaps, we introduce AMemGym, an interactive environment enabling on-policy evaluation and optimization for memory-driven personalization. AMemGym employs structured data sampling to predefine user profiles, state-dependent questions, and state evolution trajectories, enabling cost-effective generation of high-quality, evaluation-aligned interactions. LLM-simulated users expose latent states through role-play while maintaining structured state consistency. Comprehensive metrics based on structured data guide both assessment and optimization of assistants. Extensive experiments reveal performance gaps in existing memory systems (e.g., RAG, long-context LLMs, and agentic memory) and corresponding reasons. AMemGym not only enables effective selection among competing approaches but also can potentially drive the self-evolution of memory management strategies. By bridging structured state evolution with free-form interactions, our framework provides a scalable, diagnostically rich environment for advancing memory capabilities in conversational agents.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces AMemGym, an interactive environment for on-policy evaluation and optimization of memory-driven personalization in conversational agents. It resides in the 'Interactive and On-Policy Memory Evaluation' leaf of the taxonomy, which currently contains only this single paper. This positioning highlights a sparse research direction: while the broader 'Memory Evaluation and Benchmarking' branch includes static dataset approaches like Long-Term Memory Evaluation and MemoryBank, AMemGym occupies a unique niche focused on dynamic, agent-environment interaction loops rather than offline assessment.

The taxonomy reveals that most memory evaluation work clusters in the sibling leaf 'Long-Term Conversational Memory Datasets,' which emphasizes pre-collected, static benchmarks. Neighboring branches address memory architecture (hierarchical systems, biologically-inspired frameworks) and retrieval mechanisms (RAG-based, attention-driven access). AMemGym diverges by prioritizing interactive testbeds over structural design or retrieval optimization, connecting instead to broader agent evaluation trends (Agent Evaluation Survey) and long-horizon reinforcement learning. Its scope explicitly excludes static dataset evaluations and multi-turn benchmarks without memory focus, carving out a distinct methodological space.

Among twenty-seven candidates examined, the contribution-level analysis shows varied novelty. The interactive environment itself (Contribution A: seven candidates, zero refutations) and the structured data sampling approach (Contribution B: ten candidates, zero refutations) appear relatively novel within the limited search scope. However, the comprehensive diagnostic metrics (Contribution C: ten candidates, one refutation) encounter some prior overlap, suggesting that metric-based memory assessment has precedent. These statistics reflect a targeted semantic search, not an exhaustive literature review, so the findings indicate novelty relative to the most semantically similar recent work rather than the entire field.

Given the limited search scope of twenty-seven candidates, AMemGym's interactive evaluation paradigm appears to address a gap in current benchmarking practices, which predominantly rely on static datasets. The single-paper leaf status and absence of refutations for the core environment contribution suggest meaningful differentiation from existing evaluation frameworks. However, the analysis does not cover all possible prior work in agent simulation or interactive testbeds outside the memory-specific literature, leaving open questions about broader precedents in reinforcement learning or human-AI interaction domains.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
27
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: memory management in long-horizon conversational agents. The field organizes around several major branches that reflect different facets of how agents store, access, and evolve information over extended interactions. Memory Architecture and Organization addresses structural designs—ranging from hierarchical and multi-granularity schemes (e.g., Multi-Granularity Memory[7]) to systems that mimic human memory processes (e.g., MemGPT[18]). Memory Retrieval and Selection focuses on mechanisms for efficiently locating relevant context, often leveraging retrieval-augmented generation or attention-based indexing (e.g., RAG-Driven Memory[37], Attentionstore[48]). Memory Dynamics and Evolution explores how memories are updated, compressed, or refined over time (e.g., Reflective Memory Management[8], Evo-Memory[47]). Meanwhile, Memory Evaluation and Benchmarking develops metrics and testbeds to assess memory performance, Application-Specific Memory Systems tailors solutions to domains like multimodal or interactive settings (e.g., Multimodal Long-Term[23]), and System-Level Memory Optimization tackles computational efficiency (e.g., Multi-turn Serving[6]). Multi-Turn Dialogue Foundations and Theoretical and Conceptual Foundations provide the conversational and cognitive underpinnings that inform these technical choices. A particularly active line of work centers on interactive and on-policy evaluation, where agents are tested in dynamic, multi-turn scenarios rather than static benchmarks. AMemGym[0] exemplifies this direction by providing an interactive testbed that measures memory capabilities through sustained agent–environment loops, contrasting with offline evaluation frameworks like Long-Term Memory Evaluation[3] or MemoryBank[4] that rely on pre-collected datasets. This emphasis on interactive assessment aligns with broader trends in agent evaluation (Agent Evaluation Survey[17]) and long-horizon reinforcement learning (Long-Horizon RL[5]), where the ability to maintain and leverage memory across many turns becomes critical. By situating memory evaluation in live interaction, AMemGym[0] addresses open questions about how well memory systems generalize beyond curated benchmarks and whether they can adapt to evolving conversational contexts—a theme echoed in works on memory dynamics and real-time retrieval.

Claimed Contributions

AMemGym interactive environment for on-policy memory evaluation

The authors introduce AMemGym, a novel framework that enables on-policy evaluation of conversational memory in LLM-based assistants. Unlike existing benchmarks that rely on static off-policy data, AMemGym uses simulated users to generate interactive conversations grounded in structured state evolution, providing a scalable and diagnostically rich environment for assessing memory capabilities.

7 retrieved papers
Structured data sampling approach for grounded interactions

The framework uses a schema-based approach to generate structured data including user profiles, state variables, evolution trajectories, and personalized responses. This structured foundation enables controlled generation of free-form LLM-driven interactions while maintaining consistency for reliable evaluation.

10 retrieved papers
Comprehensive diagnostic metrics for memory operations

The authors provide evaluation metrics that decompose memory performance into three operational stages: write, read, and utilization. These diagnostic metrics enable systematic error attribution and guide optimization of memory management strategies beyond overall accuracy scores.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

AMemGym interactive environment for on-policy memory evaluation

The authors introduce AMemGym, a novel framework that enables on-policy evaluation of conversational memory in LLM-based assistants. Unlike existing benchmarks that rely on static off-policy data, AMemGym uses simulated users to generate interactive conversations grounded in structured state evolution, providing a scalable and diagnostically rich environment for assessing memory capabilities.

Contribution

Structured data sampling approach for grounded interactions

The framework uses a schema-based approach to generate structured data including user profiles, state variables, evolution trajectories, and personalized responses. This structured foundation enables controlled generation of free-form LLM-driven interactions while maintaining consistency for reliable evaluation.

Contribution

Comprehensive diagnostic metrics for memory operations

The authors provide evaluation metrics that decompose memory performance into three operational stages: write, read, and utilization. These diagnostic metrics enable systematic error attribution and guide optimization of memory management strategies beyond overall accuracy scores.

Table of Contents

AMemGym: Interactive Memory Benchmarking for Assistants in Long-Horizon Conversations | Novelty Validation