Darwinian Memory: A Training-Free Self-Regulating Memory System for GUI Agent Evolution
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908799120441344 |
|---|---|
| author | Mi, Hongze Feng, Yibo Lu, WenJie Cao, Song Li, Jinyuan Li, Yanming Zhang, Xuelin Luo, Haotian Peng, Songyang Cui, He Tian, Tengfei Fang, Jun Chai, Hua Tan, Naiqiang |
| author_facet | Mi, Hongze Feng, Yibo Lu, WenJie Cao, Song Li, Jinyuan Li, Yanming Zhang, Xuelin Luo, Haotian Peng, Songyang Cui, He Tian, Tengfei Fang, Jun Chai, Hua Tan, Naiqiang |
| contents | Multimodal Large Language Model (MLLM) agents facilitate Graphical User Interface (GUI) automation but struggle with long-horizon, cross-application tasks due to limited context windows. While memory systems provide a viable solution, existing paradigms struggle to adapt to dynamic GUI environments, suffering from a granularity mismatch between high-level intent and low-level execution, and context pollution where the static accumulation of outdated experiences drives agents into hallucination. To address these bottlenecks, we propose the Darwinian Memory System (DMS), a self-evolving architecture that constructs memory as a dynamic ecosystem governed by the law of survival of the fittest. DMS decomposes complex trajectories into independent, reusable units for compositional flexibility, and implements Utility-driven Natural Selection to track survival value, actively pruning suboptimal paths and inhibiting high-risk plans. This evolutionary pressure compels the agent to derive superior strategies. Extensive experiments on real-world multi-app benchmarks validate that DMS boosts general-purpose MLLMs without training costs or architectural overhead, achieving average gains of 18.0% in success rate and 33.9% in execution stability, while reducing task latency, establishing it as an effective self-evolving memory system for GUI tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22528 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Darwinian Memory: A Training-Free Self-Regulating Memory System for GUI Agent Evolution Mi, Hongze Feng, Yibo Lu, WenJie Cao, Song Li, Jinyuan Li, Yanming Zhang, Xuelin Luo, Haotian Peng, Songyang Cui, He Tian, Tengfei Fang, Jun Chai, Hua Tan, Naiqiang Artificial Intelligence Multimodal Large Language Model (MLLM) agents facilitate Graphical User Interface (GUI) automation but struggle with long-horizon, cross-application tasks due to limited context windows. While memory systems provide a viable solution, existing paradigms struggle to adapt to dynamic GUI environments, suffering from a granularity mismatch between high-level intent and low-level execution, and context pollution where the static accumulation of outdated experiences drives agents into hallucination. To address these bottlenecks, we propose the Darwinian Memory System (DMS), a self-evolving architecture that constructs memory as a dynamic ecosystem governed by the law of survival of the fittest. DMS decomposes complex trajectories into independent, reusable units for compositional flexibility, and implements Utility-driven Natural Selection to track survival value, actively pruning suboptimal paths and inhibiting high-risk plans. This evolutionary pressure compels the agent to derive superior strategies. Extensive experiments on real-world multi-app benchmarks validate that DMS boosts general-purpose MLLMs without training costs or architectural overhead, achieving average gains of 18.0% in success rate and 33.9% in execution stability, while reducing task latency, establishing it as an effective self-evolving memory system for GUI tasks. |
| title | Darwinian Memory: A Training-Free Self-Regulating Memory System for GUI Agent Evolution |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.22528 |