arXiv:2606.30697v1 Announce Type: cross Abstract: Current operating systems expose interfaces optimized for human users but not for AI agents. Humans benefit from pixels, icons, windows, visual grouping, mouse movement, and keyboard shortcuts; AI agents instead need compact semantic state, grounded actions, and reliable feedback. As a result, many computer-use agents are forced to interpret screenshots, OCR output, and visual crops, introducing high token costs, visual ambiguity, latency, and coordinate uncertainty. This paper introduces LUMOS (Language Model Unified Machine-Readable Operating
The proliferation of AI agents operating on conventional operating systems highlights the urgent need for interfaces optimized for machine rather than human interaction.
This development addresses a fundamental bottleneck in AI agent performance, potentially unlocking significant efficiency gains and new capabilities for autonomous systems.
Operating systems will evolve to include dedicated semantic layers and interfaces, shifting from human-centric design to dual human-AI optimization.
- · AI agent developers
- · Operating system providers
- · Enterprises deploying automation at scale
- · Software infrastructure companies
- · Companies reliant on high-cost OCR for AI automation
- · Platforms with closed, inscrutable interfaces
- · Legacy automation tools
AI agents become significantly more efficient, reliable, and capable of complex digital interactions.
This efficiency drives an acceleration in the deployment and impact of AI agents across various industries, collapsing more white-collar workflows.
The enhanced capabilities of AI agents could lead to new forms of autonomous digital economies or necessitate new regulatory frameworks for machine-to-machine interaction.
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Read at arXiv cs.AI