OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agents
OS-SPEAR toolkit systematically analyzes OS agents' safety, performance, efficiency, and robustness, revealing trade-offs between efficiency and safety.
Key Findings
Methodology
OS-SPEAR systematically analyzes OS agents through four subsets: Safety, Performance, Efficiency, and Robustness. The Safety subset covers environmental and human-induced hazards; the Performance subset is curated via trajectory value estimation and stratified sampling; the Efficiency subset quantifies performance through temporal latency and token consumption; the Robustness subset applies cross-modal disturbances to visual and textual inputs.
Key Results
- Efficiency often comes at the cost of safety or robustness. Specialized agents outperform general-purpose models in performance.
- Larger models tend to exhibit better safety performance. OS agents' robustness weaknesses vary across modalities.
- Within the same model family, improvements in inference cost offer limited gains.
Significance
OS-SPEAR provides a standardized evaluation framework, revealing trade-offs between efficiency and safety or robustness in current OS agents. This offers foundational resources for developing the next generation of reliable and efficient OS agents, with significant academic and industrial implications.
Technical Contribution
OS-SPEAR extends existing benchmarks by providing more comprehensive evaluation dimensions and introduces an automated analysis tool to generate human-readable diagnostic reports, filling gaps in safety, efficiency, and cross-modal robustness evaluation.
Novelty
OS-SPEAR is the first to systematically evaluate the four critical dimensions of OS agents, particularly providing new standards for multi-modal robustness, offering significant innovation compared to existing work.
Limitations
- The toolkit's applicability in extreme environments is limited, potentially failing to capture all potential risks comprehensively.
- Applicability may vary across different model scales.
Future Work
Future work could expand OS-SPEAR's applicability to cover more real-world scenarios and further optimize the automation level of the evaluation tools.
AI Executive Summary
The rapid evolution of Multimodal Large Language Models (MLLMs) has catalyzed a paradigm shift from passive text generation to active behavioral execution, particularly in OS agents navigating complex Graphical User Interfaces (GUIs). However, the transition of these agents into trustworthy daily partners is hindered by a lack of rigorous evaluation regarding safety, efficiency, and multi-modal robustness. Current benchmarks suffer from significant limitations, including narrow safety scenarios, noisy trajectory labeling, and reliance on single-modality robustness metrics. To bridge this gap, we propose OS-SPEAR, a comprehensive toolkit designed for the systematic analysis of OS agents across four critical dimensions: Safety, Performance, Efficiency, and Robustness. OS-SPEAR introduces four specialized subsets: a Safety subset encompassing diverse environment- and human-induced hazards; a Performance subset curated via trajectory value estimation and stratified sampling; an Efficiency subset quantifying performance through the dual lenses of temporal latency and token consumption; and a Robustness subset that applies cross-modal disturbances to both visual and textual inputs. Additionally, we provide an automated analysis tool to generate human-readable diagnostic reports. We conduct an extensive evaluation of 22 popular OS agents using OS-SPEAR. Our empirical results reveal critical insights into the current landscape: notably, a prevalent trade-off between efficiency and safety or robustness, the performance superiority of specialized agents over general-purpose models, and varying robustness vulnerabilities across different modalities. By providing a multidimensional ranking and a standardized evaluation framework, OS-SPEAR offers a foundational resource for developing the next generation of reliable and efficient OS agents. The dataset and codes are available at https://github.com/Wuzheng02/OS-SPEAR.
Deep Analysis
Background
The development of Multimodal Large Language Models (MLLMs) has enabled OS agents to perform tasks in complex Graphical User Interfaces (GUIs). Existing research mainly focuses on task completion rates but lacks rigorous evaluation in terms of safety, efficiency, and robustness. Current benchmarks have limitations in safety scenarios, trajectory labeling, and robustness metrics.
Core Problem
OS agents face challenges in becoming trustworthy daily partners due to a lack of rigorous evaluation regarding safety, efficiency, and multi-modal robustness. Current benchmarks have significant limitations in narrow safety scenarios, noisy trajectory labeling, and reliance on single-modality robustness metrics.
Innovation
OS-SPEAR systematically analyzes OS agents through four subsets: Safety, Performance, Efficiency, and Robustness. The Safety subset covers environmental and human-induced hazards; the Performance subset is curated via trajectory value estimation and stratified sampling; the Efficiency subset quantifies performance through temporal latency and token consumption; the Robustness subset applies cross-modal disturbances to visual and textual inputs.
Methodology
- �� Safety subset: covers environmental and human-induced hazards.
- �� Performance subset: curated via trajectory value estimation and stratified sampling.
- �� Efficiency subset: quantifies performance through temporal latency and token consumption.
- �� Robustness subset: applies cross-modal disturbances to visual and textual inputs.
Experiments
We conducted extensive evaluations of 22 popular OS agents using OS-SPEAR. The experimental design includes evaluations across the four subsets, covering safety, performance, efficiency, and robustness. We used models of different scales and recorded rankings for each subset and overall rankings.
Results
Empirical results reveal critical insights into the current landscape: a prevalent trade-off between efficiency and safety or robustness, the performance superiority of specialized agents over general-purpose models, and varying robustness vulnerabilities across different modalities.
Applications
OS-SPEAR offers foundational resources for developing the next generation of reliable and efficient OS agents. It can be used to evaluate and improve the safety, efficiency, and robustness of existing agents, helping developers identify and address potential risks.
Limitations & Outlook
OS-SPEAR's applicability in extreme environments is limited, potentially failing to capture all potential risks comprehensively. Future work could expand OS-SPEAR's applicability to cover more real-world scenarios.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. OS-SPEAR is like a multi-functional kitchen tool that helps you stay safe, efficient, and stable during cooking. It has four functions: safety is like a smoke alarm, ensuring you don't burn your food; performance is like a recipe, helping you complete delicious dishes step by step; efficiency is like a timer, ensuring you finish cooking within the set time; robustness is like a blender, keeping the texture consistent even when adding different ingredients. Through these functions, OS-SPEAR helps OS agents run smoothly in complex environments.
ELI14 Explained like you're 14
Imagine you're playing a super complex video game, and OS-SPEAR is like a super helper. It helps you stay safe in the game, like a shield; it helps you score higher, like a game guide; it helps you save time, like a speed boost; it also helps you stay stable across different levels, like a universal key. Through these functions, OS-SPEAR makes your gaming experience smoother and more fun!
Glossary
Multimodal Large Language Model (MLLM)
A language model capable of processing multiple input modalities, such as text and images.
Used as the core technology driving OS agents in OS-SPEAR.
OS Agent
An intelligent agent capable of automating tasks within an operating system.
OS-SPEAR evaluates these agents' safety, performance, efficiency, and robustness.
Safety
The ability of an OS agent to avoid potential risks during task execution.
A key evaluation dimension in OS-SPEAR.
Performance
The efficiency and accuracy of an OS agent in completing various tasks.
Evaluated in OS-SPEAR through trajectory value estimation and stratified sampling.
Robustness
The ability of an OS agent to remain stable when facing disturbances in different input modalities.
Evaluated in OS-SPEAR by applying cross-modal disturbances.
Open Questions Unanswered questions from this research
- 1 How to improve OS agents' safety and robustness in extreme environments? Current methods have limited applicability in these scenarios.
- 2 How to enhance OS agents' efficiency without increasing computational costs?
- 3 How to further optimize OS agents' performance in multi-modal environments?
Applications
Immediate Applications
Safety Evaluation
Developers can use OS-SPEAR to quickly evaluate OS agents' safety, identify potential risks, and make improvements.
Long-term Vision
Intelligent Assistant
OS-SPEAR can help develop smarter, more reliable operating system assistants, enhancing user experience.
Abstract
The evolution of Multimodal Large Language Models (MLLMs) has shifted the focus from text generation to active behavioral execution, particularly via OS agents navigating complex GUIs. However, the transition of these agents into trustworthy daily partners is hindered by a lack of rigorous evaluation regarding safety, efficiency, and multi-modal robustness. Current benchmarks suffer from narrow safety scenarios, noisy trajectory labeling, and limited robustness metrics. To bridge this gap, we propose OS-SPEAR, a comprehensive toolkit for the systematic analysis of OS agents across four dimensions: Safety, Performance, Efficiency, and Robustness. OS-SPEAR introduces four specialized subsets: (1) a S(afety)-subset encompassing diverse environment- and human-induced hazards; (2) a P(erformance)-subset curated via trajectory value estimation and stratified sampling; (3) an E(fficiency)-subset quantifying performance through the dual lenses of temporal latency and token consumption; and (4) a R(obustness)-subset that applies cross-modal disturbances to both visual and textual inputs. Additionally, we provide an automated analysis tool to generate human-readable diagnostic reports. We conduct an extensive evaluation of 22 popular OS agents using OS-SPEAR. Our empirical results reveal critical insights into the current landscape: notably, a prevalent trade-off between efficiency and safety or robustness, the performance superiority of specialized agents over general-purpose models, and varying robustness vulnerabilities across different modalities. By providing a multidimensional ranking and a standardized evaluation framework, OS-SPEAR offers a foundational resource for developing the next generation of reliable and efficient OS agents. The dataset and codes are available at https://github.com/Wuzheng02/OS-SPEAR.