Hybrid Self-evolving Structured Memory for GUI Agents
arXiv:2603.10291v1 Announce Type: new Abstract: The remarkable progress of vision-language models (VLMs) has enabled GUI agents to interact with computers in a human-like manner. Yet real-world computer-use tasks remain difficult due to long-horizon workflows, diverse interfaces, and frequent intermediate errors. Prior work equips agents with external memory built from large collections of trajectories, but relies on flat retrieval over discrete summaries or continuous embeddings, falling short of the structured organization and self-evolving characteristics of human memory. Inspired by the brain, we propose Hybrid Self-evolving Structured Memory (HyMEM), a graph-based memory that couples discrete high-level symbolic nodes with continuous trajectory embeddings. HyMEM maintains a graph structure to support multi-hop retrieval, self-evolution via node update operations, and on-the-fly working-memory refreshing during inference. Extensive experiments show that HyMEM consistently improves
arXiv:2603.10291v1 Announce Type: new Abstract: The remarkable progress of vision-language models (VLMs) has enabled GUI agents to interact with computers in a human-like manner. Yet real-world computer-use tasks remain difficult due to long-horizon workflows, diverse interfaces, and frequent intermediate errors. Prior work equips agents with external memory built from large collections of trajectories, but relies on flat retrieval over discrete summaries or continuous embeddings, falling short of the structured organization and self-evolving characteristics of human memory. Inspired by the brain, we propose Hybrid Self-evolving Structured Memory (HyMEM), a graph-based memory that couples discrete high-level symbolic nodes with continuous trajectory embeddings. HyMEM maintains a graph structure to support multi-hop retrieval, self-evolution via node update operations, and on-the-fly working-memory refreshing during inference. Extensive experiments show that HyMEM consistently improves open-source GUI agents, enabling 7B/8B backbones to match or surpass strong closed-source models; notably, it boosts Qwen2.5-VL-7B by +22.5% and outperforms Gemini2.5-Pro-Vision and GPT-4o.
Executive Summary
The article introduces Hybrid Self-evolving Structured Memory (HyMEM), a novel graph-based memory architecture designed to enhance GUI agent performance by integrating discrete symbolic nodes with continuous trajectory embeddings. Traditional memory systems rely on flat retrieval, which limits adaptability and contextual understanding. HyMEM addresses this by enabling multi-hop retrieval, self-evolution through node updates, and dynamic working-memory refreshing during inference. Empirical results demonstrate significant improvements over existing open-source and closed-source models, particularly with large-scale backbones. This advancement represents a meaningful step toward more human-like memory-like behavior in AI agents.
Key Points
- ▸ HyMEM combines discrete symbolic nodes with continuous trajectory embeddings
- ▸ Supports multi-hop retrieval and self-evolution via node updates
- ▸ Empirical validation shows +22.5% improvement with Qwen2.5-VL-7B and outperforms Gemini2.5-Pro-Vision and GPT-4o
Merits
Innovation in Memory Architecture
HyMEM introduces a novel hybrid graph-based structure that mimics human memory characteristics, offering more contextual richness and adaptability than traditional flat retrieval systems.
Empirical Validation
The results are compelling—significant performance gains across multiple benchmark models validate the efficacy of the proposed architecture.
Demerits
Complexity of Implementation
The integration of graph-based structures with continuous embeddings may introduce computational overhead and implementation complexity, potentially limiting scalability in resource-constrained environments.
Generalizability Concerns
While results are strong on specified benchmarks, broader applicability across diverse interface types or non-GUI environments remains unproven.
Expert Commentary
This paper represents a substantive contribution to the field of agent-based AI. The conceptual leap from flat embeddings to a hybrid graph-based memory system is both theoretically grounded and empirically validated. The authors successfully bridge a critical gap between human-inspired memory constructs and computational feasibility. What distinguishes HyMEM is not merely the hybrid structure but the operationalization of self-evolution via node updates—a mechanism that aligns with human memory’s dynamic recalibration. This is particularly noteworthy given the persistent challenge of maintaining contextual coherence across long-horizon tasks. The results suggest that future agent architectures may need to incorporate memory systems that support both symbolic abstraction and continuous representation simultaneously. The implications extend beyond GUI interactions into broader domains requiring adaptive, context-aware reasoning. This work should be considered a landmark in the evolution of agent memory design.
Recommendations
- ✓ Integrate HyMEM into open-source agent repositories as a configurable memory module.
- ✓ Conduct comparative studies across non-GUI interfaces (e.g., command-line, web APIs) to assess generalizability.
- ✓ Explore real-time adaptation of HyMEM’s update mechanisms for dynamic environments.