arXiv:2606.11520v1 Announce Type: new Abstract: Training capable OS agents requires data that simultaneously captures structured user intents, multi-turn task delegation, and grounded tool execution--properties absent from existing datasets. We propose ISE (Intent -> Simulate -> Execute), a three-stage synthesis paradigm that addresses these gaps jointly. Stage 1 constructs roughly 50000 structured intents via a 4D framework (Persona x Domain x Task x Complexity); after deduplication the pool contains 43956 unique intents and attains a Vendi Score of 61.57 over the entire pool on mpnet-base-v2
The rapid advancement of large language models is driving the urgent need for more sophisticated AI agents that can interact with operating systems effectively.
Improved OS agents could significantly automate complex digital tasks, affecting productivity across numerous industries and accelerating the disruption of white-collar workflows.
The ability to generate robust training data for multi-turn OS agents opens pathways to more powerful and autonomous AI systems than previously feasible.
- · AI development companies
- · Software automation sector
- · Enterprises adopting AI agents
- · Tasks requiring manual multi-step digital interaction
- · SaaS layers providing intermediary workflow steps
The availability of high-quality, execution-grounded data will lead to more capable and reliable OS agents.
These agents will begin to autonomously handle complex, multi-application tasks currently performed by humans.
Pervasive OS agents could fundamentally reshape software interfaces, operating systems, and the nature of digital work itself.
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Read at arXiv cs.CL