Brief context
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Original paper
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Computer-use agents hold the promise of assisting in a wide range of digital economic activities. However, current research has largely focused on short-horizon tasks over a limited set of software with limited economic value, such as basic e-commerce and OS-configuration tasks. A key reason is that creating environments for complex software requires significant time and human effort, and therefore does not scale. To address this, we introduce Gym-Anything, a framework for converting any software into an interactive computer-use environment. We frame environment creation itself as a multi-agent task: a coding agent writes setup scripts, downloads real-world data, and configures the software, while producing evidence of correct setup. An independent audit agent then verifies evidence for the environment setup against a quality checklist. Using a taxonomy of economically valuable occupations grounded in U.S. GDP data, we apply this pipeline to 200 software applications with broad occupational coverage. The result is CUA-World, a collection of over 10K long-horizon tasks spanning domains from medical science and astronomy to engineering and enterprise systems, each configured with realistic data along with train and test splits. CUA-World also includes CUA-World-Long, a challenging long-horizon benchmark with tasks often requiring over 500 steps, far exceeding existing benchmarks. Distilling successful trajectories from the training split into a 2B vision-language model outperforms models 2$\times$ its size. We also apply the same auditing principle at test time: a separate VLM reviews completed trajectories and provides feedback on what remains, improving Gemini-3-Flash on CUA-World-Long from 11.5% to 14.0%. We release all code, infrastructure, and benchmark data to facilitate future research in realistic computer-use agents.
Executive brief
A short business-reader brief that explains why the paper matters now and what to watch or do next.
Why this is worth your attention
The bottleneck for computer-use agents may be shifting from model capability to environment supply: this paper shows a credible way to turn real business software into trainable, testable agent environments at much larger scale than hand-built benchmarks. If that holds up, it makes enterprise automation R&D less dependent on bespoke demo setups and more like a data and infrastructure problem—something product, ops, and platform teams can systematically invest in. The catch is equally important: the benchmark they create is hard enough that today’s best agents still fail most long, realistic workflows, so this is better read as an acceleration of the path to useful software agents than proof they are ready to replace knowledge workers now.
- A common assumption has been that serious computer-use automation is bottlenecked mainly by model intelligence; this paper argues a big hidden bottleneck is the lack of realistic environments to train and evaluate on. If that is right, advantage shifts toward whoever can cheaply generate, verify, and refresh software-specific task corpora, not just whoever has the best frontier model.
- If an agent vendor claims broad enterprise readiness, ask whether they can stand up realistic environments with real data, reproducible setup scripts, and independent verification—not just scripted demos in a few apps. This paper’s strongest operational idea is the creation-plus-audit loop, because agents are unreliable narrators of what they actually configured.
- The most commercially interesting result is not raw frontier-model performance; it is that distilled trajectories from this benchmark helped a 2B vision-language model beat open models up to 2× its size. If replicated, that points to a cheaper path for domain-specific software agents: better workflow data and distillation, not always bigger models.
- The paper is clear that realistic multi-step software work is still brittle: long tasks often run hundreds of steps, top models pass only a minority of them, and lifting cost caps helps but makes trajectories expensive. That means near-term value is more likely in bounded copilots, QA loops, and audited partial automation than in fully autonomous back-office replacement.
- The benchmark is broad, but it still excludes much commercial software and substitutes some non-sandboxable tools with close alternatives. So the paper is strong evidence that environment generation can scale, but weaker evidence that current results cleanly transfer to the exact licensed systems and controls used inside large enterprises.
Affiliations
Institution names extracted from the brief's PDF summary call.
Carnegie Mellon University
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Evidence ledger
The strongest claims in the brief, along with the confidence and citation depth behind them.
Gym-Anything standardizes arbitrary software into reusable agent environments with setup scripts and config files.
The paper creates a large GDP-grounded benchmark of realistic software tasks spanning 200 applications and 10,000+ tasks.
Current frontier models are far from robust on realistic long-horizon software tasks.
Audit-style verification helps both environment setup and test-time task completion, but gains are incremental rather than transformative.
Distillation from collected trajectories can improve small models, suggesting data quality may matter as much as model size in this setting.
Benchmark coverage is broad but not fully representative of real enterprise software because of sandboxability constraints and substitutions.
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