A brand new framework from researchers at The College of Hong Kong (HKU) and collaborating establishments gives an open supply basis for creating sturdy AI brokers that may function computer systems. The framework, referred to as OpenCUA, contains the instruments, knowledge, and recipes for scaling the event of computer-use brokers (CUAs).
Fashions educated utilizing this framework carry out strongly on CUA benchmarks, outperforming present open supply fashions and competing intently with closed brokers from main AI labs like OpenAI and Anthropic.
The problem of constructing computer-use brokers
Pc-use brokers are designed to autonomously full duties on a pc, from navigating web sites to working complicated software program. They will additionally assist automate workflows within the enterprise. Nevertheless, essentially the most succesful CUA techniques are proprietary, with important particulars about their coaching knowledge, architectures, and improvement processes saved personal.
“As the lack of transparency limits technical advancements and raises safety concerns, the research community needs truly open CUA frameworks to study their capabilities, limitations, and risks,” the researchers state of their paper.
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On the identical time, open supply efforts face their very own set of hurdles. There was no scalable infrastructure for gathering the varied, large-scale knowledge wanted to coach these brokers. Present open supply datasets for graphical person interfaces (GUIs) have restricted knowledge, and plenty of analysis tasks present inadequate element about their strategies, making it tough for others to copy their work.
Based on the paper, “These limitations collectively hinder advances in general-purpose CUAs and restrict a meaningful exploration of their scalability, generalizability, and potential learning approaches.”
Introducing OpenCUA
OpenCUA framework Supply: XLANG Lab at HKU
OpenCUA is an open supply framework designed to handle these challenges by scaling each the info assortment and the fashions themselves. At its core is the AgentNet Instrument for recording human demonstrations of laptop duties on completely different working techniques.
The device streamlines knowledge assortment by working within the background on an annotator’s private laptop, capturing display screen movies, mouse and keyboard inputs, and the underlying accessibility tree, which gives structured details about on-screen components. This uncooked knowledge is then processed into “state-action trajectories,” pairing a screenshot of the pc (the state) with the person’s corresponding motion (a click on, key press, and so forth.). Annotators can then evaluate, edit, and submit these demonstrations.
AgentNet device Supply: XLang Lab at HKU
Utilizing this device, the researchers collected the AgentNet dataset, which comprises over 22,600 process demonstrations throughout Home windows, macOS, and Ubuntu, spanning greater than 200 functions and web sites. “This dataset authentically captures the complexity of human behaviors and environmental dynamics from users’ personal computing environments,” the paper notes.
Recognizing that screen-recording instruments increase vital knowledge privateness issues for enterprises, the researchers designed the AgentNet Instrument with safety in thoughts. Xinyuan Wang, co-author of the paper and PhD pupil at HKU, defined that they carried out a multi-layer privateness safety framework. “First, annotators themselves can fully observe the data they generate… before deciding whether to submit it,” he informed VentureBeat. The information then undergoes handbook verification for privateness points and automatic scanning by a big mannequin to detect any remaining delicate content material earlier than launch. “This layered process ensures enterprise-grade robustness for environments handling sensitive customer or financial data,” Wang added.
To speed up analysis, the staff additionally curated AgentNetBench, an offline benchmark that gives a number of appropriate actions for every step, providing a extra environment friendly strategy to measure an agent’s efficiency.
A brand new recipe for coaching brokers
The OpenCUA framework introduces a novel pipeline for processing knowledge and coaching computer-use brokers. Step one converts the uncooked human demonstrations into clear state-action pairs appropriate for coaching vision-language fashions (VLMs). Nevertheless, the researchers discovered that merely coaching fashions on these pairs yields restricted efficiency positive factors, even with giant quantities of information.
OpenCUA chain-of-thought pipeline Supply: XLang Lab at HKU
The important thing perception was to enhance these trajectories with chain-of-thought (CoT) reasoning. This course of generates an in depth “inner monologue” for every motion, which incorporates planning, reminiscence, and reflection. This structured reasoning is organized into three ranges: a high-level commentary of the display screen, reflective ideas that analyze the state of affairs and plan the following steps, and at last, the concise, executable motion. This strategy helps the agent develop a deeper understanding of the duties.
“We find natural language reasoning crucial for generalizable computer-use foundation models, helping CUAs internalize cognitive capabilities,” the researchers write.
This knowledge synthesis pipeline is a common framework that may be tailored by corporations to coach brokers on their very own distinctive inside instruments. Based on Wang, an enterprise can report demonstrations of its proprietary workflows and use the identical “reflector” and “generator” pipeline to create the mandatory coaching knowledge. “This allows them to bootstrap a high-performing agent tailored to their internal tools without needing to handcraft reasoning traces manually,” he defined.
Placing OpenCUA to the check
The researchers utilized the OpenCUA framework to coach a spread of open supply VLMs, together with variants of Qwen and Kimi-VL, with parameter sizes from 3 billion to 32 billion. The fashions have been evaluated on a collection of on-line and offline benchmarks that check their means to carry out duties and perceive GUIs.
The 32-billion-parameter mannequin, OpenCUA-32B, established a brand new state-of-the-art success charge amongst open supply fashions on the OSWorld-Verified benchmark. It additionally surpassed OpenAI’s GPT-4o-based CUA and considerably closed the efficiency hole with Anthropic’s main proprietary fashions.
OpenCUA exhibits huge enchancment over base fashions (left) whereas competing with main CUA fashions (proper) Supply: XLANG Lab at HKU
For enterprise builders and product leaders, the analysis affords a number of key findings. The OpenCUA methodology is broadly relevant, enhancing efficiency on fashions with completely different architectures (each dense and mixture-of-experts) and sizes. The educated brokers additionally present sturdy generalization, performing nicely throughout a various vary of duties and working techniques.
Based on Wang, the framework is especially fitted to automating repetitive, labor-intensive enterprise workflows. “For example, in the AgentNet dataset, we already capture a few demonstrations of launching EC2 instances on Amazon AWS and configuring annotation parameters on MTurk,” he informed VentureBeat. “These tasks involve many sequential steps but follow repeatable patterns.”
Nevertheless, Wang famous that bridging the hole to dwell deployment requires addressing key challenges round security and reliability. “The biggest challenge in real deployment is safety and reliability: the agent must avoid mistakes that could inadvertently alter system settings or trigger harmful side effects beyond the intended task,” he stated.
The researchers have launched the code, dataset, and weights for his or her fashions.
As open supply brokers constructed on frameworks like OpenCUA change into extra succesful, they might basically evolve the connection between information staff and their computer systems. Wang envisions a future the place proficiency in complicated software program turns into much less necessary than the flexibility to obviously articulate objectives to an AI agent.
He described two main modes of labor: “offline automation, where the agent leverages its broader software knowledge to pursue a task end-to-end,” and “online collaboration, where the agent responds in real-time and works side by side with the human, much like a colleague.” Mainly, the people will present the strategic “what,” whereas more and more refined AI brokers deal with the operational “how.”
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