Cross-Software Generalization

Summary: Cross-software generalization refers to the ability of computer-use agents trained on specific software applications to successfully perform tasks on entirely different, previously unseen software. This capability is crucial for developing practical AI agents that can work across diverse digital environments rather than being limited to their training domains.

Overview

Cross-software generalization represents one of the most challenging aspects of developing robust Computer-Use Agents. While agents may achieve reasonable performance on software they were trained on, their ability to transfer learned interaction patterns, interface understanding, and task completion strategies to completely new applications remains limited.

The concept gained prominence through research using frameworks like Gym-Anything, which enables systematic testing of agent capabilities across hundreds of different software applications. This testing reveals the gap between controlled, single-application performance and real-world deployment scenarios where agents must navigate unfamiliar interfaces and workflows.

Cross-software generalization is particularly important because real-world digital work environments are highly heterogeneous. Users don't work with just one application—they switch between productivity software, web browsers, specialized tools, and custom applications throughout their workflows. An agent that can only operate within its training environment has limited practical utility.

Key Details

Performance Gaps: Even frontier models like Gemini-3-Flash achieve only 22.6% pass rates on standard cross-software tasks, demonstrating the significant challenge of generalizing across different applications. This drops further to 7.5% on Long-Horizon Task Planning scenarios involving unfamiliar software.

Training Data Impact: Research shows that models trained on diverse software environments through Trajectory Distillation can outperform larger models that lack this cross-domain training experience. A 2B parameter model trained on CUA-World data outperformed models twice its size, highlighting the importance of diverse training environments over raw model capacity.

Evaluation Methodology: Cross-software generalization is typically evaluated using held-out software applications that were not present during training. The CUA-World Benchmark provides a systematic approach with 200+ software applications selected based on GDP-Grounded Benchmarking to ensure economic relevance and diversity.

Common Failure Modes: Agents often struggle with unfamiliar interface elements, different keyboard shortcuts, varied menu structures, and application-specific workflows when encountering new software. They may also prematurely claim task completion when facing unexpected interface behaviors.

Relationships

• Computer-Use Agents — the primary systems being evaluated for cross-software capabilities
• Multi-Agent Environment Creation — enables systematic creation of diverse software environments for testing generalization
• GDP-Grounded Benchmarking — ensures test environments represent economically relevant software diversity
• Long-Horizon Task Planning — becomes significantly more challenging when combined with unfamiliar software
• Agent Evaluation — cross-software testing is a critical component of comprehensive agent assessment
• Trajectory Distillation — training method that can improve cross-software performance through diverse examples

Sources

• sources/arxiv-260406126 — introduced the concept through systematic evaluation across 200+ software applications using the Gym-Anything framework and CUA-World benchmark