MGA: Memory-Driven GUI Agent for Observation-Centric Interaction
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913028281204736 |
|---|---|
| author | Cheng, Weihua Liu, Junming Sun, Yifei Shi, Botian Chen, Yirong Wang, Ding |
| author_facet | Cheng, Weihua Liu, Junming Sun, Yifei Shi, Botian Chen, Yirong Wang, Ding |
| contents | Multimodal Large Language Models (MLLMs) have significantly advanced GUI agents, yet long-horizon automation remains constrained by two critical bottlenecks: context overload from raw sequential trajectory dependence and architectural redundancy from over-engineered expert modules. Prevailing End-to-End and Multi-Agent paradigms struggle with error cascades caused by concatenated visual-textual histories and incur high inference latency due to redundant expert components, limiting their practical deployment. To address these issues, we propose the Memory-Driven GUI Agent (MGA), a minimalist framework that decouples long-horizon trajectories into independent decision steps linked by a structured state memory. MGA operates on an ``Observe First and Memory Enhancement`` principle, powered by two tightly coupled core mechanisms: (1) an Observer module that acts as a task-agnostic, intent-free screen state reader to eliminate confirmation bias, visual hallucinations, and perception bias at the root; and (2) a Structured Memory mechanism that distills, validates, and compresses each interaction step into verified state deltas, constructing a lightweight state transition chain to avoid irrelevant historical interference and system redundancy. By replacing raw historical aggregation with compact, fact-based memory transitions, MGA drastically reduces cognitive overhead and system complexity. Extensive experiments on OSWorld and real-world applications demonstrate that MGA achieves highly competitive performance in open-ended GUI tasks while maintaining architectural simplicity, offering a scalable and efficient blueprint for next-generation GUI automation {https://github.com/MintyCo0kie/MGA4OSWorld}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MGA: Memory-Driven GUI Agent for Observation-Centric Interaction Cheng, Weihua Liu, Junming Sun, Yifei Shi, Botian Chen, Yirong Wang, Ding Artificial Intelligence Multimodal Large Language Models (MLLMs) have significantly advanced GUI agents, yet long-horizon automation remains constrained by two critical bottlenecks: context overload from raw sequential trajectory dependence and architectural redundancy from over-engineered expert modules. Prevailing End-to-End and Multi-Agent paradigms struggle with error cascades caused by concatenated visual-textual histories and incur high inference latency due to redundant expert components, limiting their practical deployment. To address these issues, we propose the Memory-Driven GUI Agent (MGA), a minimalist framework that decouples long-horizon trajectories into independent decision steps linked by a structured state memory. MGA operates on an ``Observe First and Memory Enhancement`` principle, powered by two tightly coupled core mechanisms: (1) an Observer module that acts as a task-agnostic, intent-free screen state reader to eliminate confirmation bias, visual hallucinations, and perception bias at the root; and (2) a Structured Memory mechanism that distills, validates, and compresses each interaction step into verified state deltas, constructing a lightweight state transition chain to avoid irrelevant historical interference and system redundancy. By replacing raw historical aggregation with compact, fact-based memory transitions, MGA drastically reduces cognitive overhead and system complexity. Extensive experiments on OSWorld and real-world applications demonstrate that MGA achieves highly competitive performance in open-ended GUI tasks while maintaining architectural simplicity, offering a scalable and efficient blueprint for next-generation GUI automation {https://github.com/MintyCo0kie/MGA4OSWorld}. |
| title | MGA: Memory-Driven GUI Agent for Observation-Centric Interaction |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.24168 |