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Introducing CRAB: A Benchmark for Cross-Platform Multimodal Agents。
Abstract: Recently, spearheaded by the CAMEL-AI community, a pioneer in open-source multi-agent projects, researchers from institutions such as King Abdullah University of Science and Technology, Oxford University, University of Tokyo, Carnegie Mellon University, Stanford University, and Tsinghua University have developed a cross-platform multimodal agent benchmark framework: CRAB, innovatively enabling agents to operate multiple devices simultaneously.
With the rapid development of multimodal large language models (MLLM), many agents capable of operating graphical user interfaces (GUIs) have emerged this year. Various companies have launched their innovative solutions, creating intense competition. GUI agents, leveraging powerful visual understanding and reasoning abilities of large models, can now efficiently and flexibly complete tasks such as booking appointments, shopping, and controlling smart homes.
This raises the question: will future agents truly be able to sit in front of a computer and work on my behalf?
However, in today's era of the Internet of Everything, most work requires the coordination of multiple devices. For example, taking a photo with a phone and then transferring it to a computer for editing involves crossing two different devices (environments). Currently, these GUI agents can only operate on a single device, making what is an easy task for humans exceedingly difficult for today's agents.
Researchers from the CAMEL-AI community noticed this problem and proposed the first cross-environment, multi-device agent benchmark framework—CRAB, the CRoss-environment Agent Benchmark.
The CAMEL framework (https://github.com/camel-ai) developed by the CAMEL-AI community is one of the earliest open-source multi-agent projects based on large language models. Therefore, community members are researchers and engineers with rich research and practical experience in the field of agents.
In CRAB, the authors not only designed a network-based multi-environment architecture that enables agents to operate multiple devices simultaneously to complete tasks, but also proposed two new technologies to address the issues existing in current agent benchmarks: the graph evaluator and task synthesis. CRAB is not only a brand new benchmark tool but also provides an interaction protocol and its implementation between the environment and agents, which is expected to become an important foundation for agents in practical fields.
The authors believe that CRAB will become one of the standards for evaluating GUI agents in the future, and thus have put considerable effort into improving the framework's usability. The entire codebase adopts a modular design, with the configuration of each environment abstracted into independent and reusable components. Users can easily and quickly build multiple custom environments like building blocks and create their own benchmarks based on them.
For users who wish to evaluate their agents' performance using CRAB, the authors thoughtfully provide a hard disk image on the Google Cloud Platform. With just one click, all the tedious configurations of virtual machines, deep learning models, Python packages, etc., will be completed automatically, allowing users to immediately engage in important experiments.
Currently, CRAB's paper has been published on Arxiv, and the related code and data are open-sourced on the CAMEL-AI community's GitHub.
GitHub repository: https://github.com/camel-ai/crab
What does CRAB look like in operation? Let's take a look at the video below:
First, the system extracts tasks from the dataset, passes the task instructions to the main agent, and initializes the corresponding graph evaluator in CRAB.
The workflow is a loop: the main agent observes, plans, and instructs the sub-agents; each sub-agent corresponds to an environment. In the diagram, two sub-agents are responsible for the Ubuntu system computer and the Android system phone, respectively, and the sub-agents perform operations in their respective platforms.
The graph evaluator monitors the state of each environment in the platform, updates the progress of the agent in completing the task after each operation, and outputs evaluation metrics.
Having understood how CRAB works, it's time to see how current models perform on this new benchmark. The authors introduced CRAB-Benchmark-v0, a dataset supporting two environments with a total of 100 tasks, and tested several state-of-the-art models.
As shown, the best-performing GPT-4o scored only 35.26 (CR refers to completion rate).
Cross-platform tasks are much more complex than single-platform tasks, and achieving the top score already demonstrates the outstanding ability of the GPT-4 series models in solving practical problems. However, we believe that emerging new methods and models can achieve better scores on CRAB, truly becoming efficient tools for solving real-world problems.
CRAB provides a comprehensive interactive agent evaluation framework. Through CRAB's foundational setup, agents can operate on various devices and platforms simultaneously, efficiently completing tasks in multiple isolated systems.
The authors propose a new evaluation method called the graph evaluator, which differs from traditional methods based on final goals or action trajectories. The graph evaluator checks the intermediate states of task completion by breaking down tasks into multiple sub-goals.
Each sub-goal is assigned an evaluation function to verify its completion, with each sub-goal being treated as a node in the graph evaluator.
The graph structure describes the precedence and parallel relationships between sub-goals, thus providing fine-grained evaluation metrics; at the same time, the independent evaluation function for each sub-goal inherently adapts to multiple platforms.
The table above compares CRAB with existing frameworks, including several key capabilities involved in testing:
Based on the CRAB framework, the authors developed a benchmark dataset, CRAB Benchmark v0, supporting Android and Ubuntu environments.
The benchmark includes 100 real-world tasks, covering various levels of difficulty for cross-platform and single-platform tasks. Tasks involve a variety of common issues and use multiple practical applications and tools, including but not limited to calendars, emails, maps, web browsers, and terminals, also replicating common coordination methods between smartphones and computers.
Assume an agent autonomously executes tasks on a device (such as a desktop). The device is usually equipped with input devices (like a mouse and keyboard) and output devices (like a screen) for human-computer interaction. The authors refer to a device or application with a fixed input method and output method as an environment.
Formally, a single environment can be defined as a reward-free partially observable Markov decision process (Reward-free POMDP), represented by a tuple M := (S, A, T, O), where S represents the state space, A represents the action space, T: S × A → S is the transition function, and O is the observation space.
Considering the collaborative nature of multiple devices in real-world scenarios, multiple platforms can be combined into a set M = {M1, M2, ..., Mn}, where n is the number of platforms, and each platform can be represented as Mj = (Sj, Aj, Tj, Oj).
A task requiring operation across multiple platforms can be formalized as a tuple (M, I, R), where M is the platform set, I is the task goal described in natural language, and R is the task reward function.
The authors call the algorithm responsible for completing the task the agent system. Each agent in the agent system uses a fixed backend model and predefined system prompt, retaining its dialogue history. The agent system can be a single agent or a multi-agent system with multiple agents cooperating.
Breaking down complex tasks into simpler sub-tasks is an effective method for prompting large language models. The authors introduced this concept into the benchmark field, breaking down complex tasks into sub-tasks with precedence and parallel relationships, known as the Graph of Decomposed Tasks (GDT) as shown above.
GDT uses a graph-based task decomposition method: using a DAG structure to represent decomposed sub-tasks.
In GDT, each node is a sub-task, formalized as a tuple (m, i, r), where m specifies the environment for executing the sub-task, i provides natural language instructions, and r represents the reward function. The reward function evaluates the state of environment m and outputs a boolean value to determine if the sub-task is completed. Edges in GDT represent the precedence relationships between sub-tasks.
To evaluate the capabilities of large language models as agents, most benchmarks rely solely on the final state of the platform after the agent's operations.
Simply judging whether the final goal is successful or failed is obviously not fair; it's like in a math exam, even if you can't solve the big problem, you should get points for writing some solution steps.
Another method is trajectory-based matching, comparing the agent's operations with a predefined standard operation sequence (Label) for each task.
However, in real-world systems, tasks may have multiple valid execution paths. For example, copying a file can be done using a file manager or a command line. Specifying a unique correct path is unfair to agents that achieve the goal in different ways.
Therefore, this paper adopts a graph evaluator synchronized with platform states, tracking the agent's progress through the current state of sub-task completion.
In addition to the traditional success rate (SR), which marks a task as successful only when all sub-tasks are completed, the authors introduced three metrics to measure the agent's performance and efficiency:
To run in Crab Benchmark-v0, the backend model needs to support the following features:
The experiments selected four multimodal models that meet these criteria: GPT-4o, GPT-4 Turbo, Gemini 1.5 Pro, and Claude 3 Opus.
To compare the performance differences between multi-agent and single-agent systems, the paper designed three different agent systems:
The combinations of different agent systems and backend models provide multiple dimensions for comparison. Additionally, the paper compares the performance of models in tasks involving different platforms:
Ubuntu single-platform tasks:
Android single-platform tasks:
Cross-platform tasks:
Through data analysis, the paper draws several conclusions:
Performance Differences Among Models:
Efficiency Metrics Reflect Different Characteristics of Models:
Evaluation of Termination Reasons Indicates Areas for Improvement:
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